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Publisher: Springer-Verlag (Total: 2352 journals)

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Showing 1 - 200 of 2352 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 2)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 23, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 17, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 27, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 29, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 19, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 3, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 11, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 13, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 21, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 7, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 1, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 4, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 3, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 25, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 17, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 5, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 59, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 30, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 4, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 45, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 11, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 21, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 13, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 9, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 29, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 9, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 17, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 5, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 14, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 6)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 3, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 20, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 17, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 32, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 5, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
Annals of Regional Science     Hybrid Journal   (Followers: 8, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 15, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 9, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 44, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 18, SJR: 0.21, CiteScore: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 5, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 6, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 9, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 8, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 66, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 10, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 24, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 22, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 7, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 26, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 36, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 21, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 65, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 153, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 17, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 9, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 9, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 16, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 2, SJR: 0.766, CiteScore: 1)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 18, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 13, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 3, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 10)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 5, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  
Astronomy and Astrophysics Review     Hybrid Journal   (Followers: 22, SJR: 3.385, CiteScore: 5)

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Journal Cover
Applied Intelligence
Journal Prestige (SJR): 0.6
Citation Impact (citeScore): 2
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7497 - ISSN (Online) 0924-669X
Published by Springer-Verlag Homepage  [2352 journals]
  • Programming model-based method for ranking objects from group decision
           making with interval-valued hesitant fuzzy preference relations
    • Authors: Yuning Zhang; Jie Tang; Fanyong Meng
      Pages: 837 - 857
      Abstract: Interval-valued hesitant fuzzy preference relations (IVHFPRs) are useful that allow decision makers to apply several intervals in [0, 1] to denote the uncertain hesitation preference. To derive the reasonable ranking order from group decision making with preference relations, two topics must be considered: consistency and consensus. This paper focuses on group decision making with IVHFPRs. First, a multiplicative consistency concept for IVHFPRs is defined. Then, programming models for judging the consistency of IVHFPRs are constructed. Meanwhile, an approach for deriving the interval fuzzy priority weight vector is introduced that adopts the consistency probability distribution as basis. Subsequently, this paper builds several multiplicative consistency-based programming models for estimating the missing values in incomplete IVHFPRs. A consensus index is introduced to measure the agreement degree between individual IVHFPRs, and a method for increasing the consensus level is presented. Finally, a multiplicative consistency-and-consensus-based group decision-making method with IVHFPRs is offered, and a practical decision-making problem is selected to show the application of the new method.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1292-1
      Issue No: Vol. 49, No. 3 (2019)
       
  • Content-aware point-of-interest recommendation based on convolutional
           neural network
    • Authors: Shuning Xing; Fang’ai Liu; Qianqian Wang; Xiaohui Zhao; Tianlai Li
      Pages: 858 - 871
      Abstract: Point-of-interest (POI) recommendation has become an important approach to help people discover attractive locations. But the extreme sparsity of the user-POI matrix creates a severe challenge. To address this challenge, researchers have begun to explore the review content information for POI recommendations. Existing methods are based on bag-of-words or embedding techniques which leads to a shallow understanding of user preference. In order to capture valuable information about user preference, we propose a content-aware POI recommendation based on convolutional neural network (CPC). We utilize a convolutional neural network as the foundation of a unified POI recommendation framework and introduce the three types of content information, including POI properties, user interests and sentiment indications. The experimental results indicate that convolutional neural network is very capable of capturing semantic and sentiment information from review content and demonstrate that the relevant information in reviews can improve POI recommendation performance on location-based social networks.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1276-1
      Issue No: Vol. 49, No. 3 (2019)
       
  • Feature selection based on conditional mutual information: minimum
           conditional relevance and minimum conditional redundancy
    • Authors: HongFang Zhou; Yao Zhang; YingJie Zhang; HongJiang Liu
      Pages: 883 - 896
      Abstract: Feature selection is a process that selects some important features from original feature set. Many existing feature selection algorithms based on information theory concentrate on maximizing relevance and minimizing redundancy. In this paper, relevance and redundancy are extended to conditional relevance and conditional redundancy. Because of the natures of the two conditional relations, they tend to produce more accurate feature relations. A new frame integrating the two conditional relations is built in this paper and two new feature selection methods are proposed, which are Minimum Conditional Relevance-Minimum Conditional Redundancy (MCRMCR) and Minimum Conditional Relevance-Minimum Intra-Class Redundancy (MCRMICR) respectively. The proposed methods can select high class-relevance and low-redundancy features. Experimental results for twelve datasets verify the proposed methods perform better on feature selection and have high classification accuracy.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1305-0
      Issue No: Vol. 49, No. 3 (2019)
       
  • A study on novel filtering and relationship between input-features and
           target-vectors in a deep learning model for stock price prediction
    • Authors: Yoojeong Song; Jae Won Lee; Jongwoo Lee
      Pages: 897 - 911
      Abstract: From past to present, the prediction of stock price in stock market has been a knotty problem. Many researchers have made various attempts and studies to predict stock prices. The prediction of stock price in stock market has been of concern to researchers in many disciplines, including economics, mathematics, physics, and computer science. This study intends to learn fluctuation of stock prices in stock market by using recently spotlighted techniques of deep learning to predict future stock price. In previous studies, we have used price-based input-features to measure performance changes in deep learning models. Results of this studies have revealed that the performance of stock price models would change according to varied input-features configured based on stock price. Therefore, we have concluded that more novel input-feature in deep learning model is needed to predict patterns of stock price fluctuation more precisely. In this paper, for predicting stock price fluctuation, we design deep learning model using 715 novel input-features configured on the basis of technical analyses. The performance of the prediction model was then compared to another model that employed simple price-based input-features. Also, rather than taking randomly collected set of stocks, stocks of a similar pattern of price fluctuation were filtered to identify the influence of filtering technique on the deep learning model. Finally, we compared and analyzed the performances of several models using different configuration of input-features and target-vectors.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1308-x
      Issue No: Vol. 49, No. 3 (2019)
       
  • Influence maximization on signed networks under independent cascade model
    • Authors: Wei Liu; Xin Chen; Byeungwoo Jeon; Ling Chen; Bolun Chen
      Pages: 912 - 928
      Abstract: Influence maximization problem is to find a subset of nodes that can make the spread of influence maximization in a social network. In this work, we present an efficient influence maximization method in signed networks. Firstly, we address an independent cascade diffusion model in the signed network (named SNIC) for describing two opposite types of influence spreading in a signed network. We define the independent propagation paths to simulate the influence spreading in SNIC model. Particularly, we also present an algorithm for constructing the set of spreading paths and computing their probabilities. Based on the independent propagation paths, we define an influence spreading function for a seed as well as a seed set, and prove that the spreading function is monotone and submodular. A greedy algorithm is presented to maximize the positive influence spreading in the signed network. We verify our algorithm on the real-world large-scale networks. Experiment results show that our method significantly outperforms the state-of-the-art methods, particularly can achieve more positive influence spreading.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1303-2
      Issue No: Vol. 49, No. 3 (2019)
       
  • Design of fuzzy radial basis function neural network classifier based on
           information data preprocessing for recycling black plastic wastes:
           comparative studies of ATR FT-IR and Raman spectroscopy
    • Authors: Jong-Soo Bae; Sung-Kwun Oh; Witold Pedrycz; Zunwei Fu
      Pages: 929 - 949
      Abstract: As large amounts of plastics are widely used in diverse areas of industry, the amount of plastic waste, including black plastics, continues to increase. In this situation, the necessity of useful recycling having limited resources gradually increases. The design of plastic classification systems for plastics recycling becomes more important to effectively address recycling activities. Until now, conventional sorting systems based on the near infrared ray technology have been used to classify plastic wastes. However, the classification of black plastic waste still remains a challenge because such materials do not reflect sufficient signals due to the absorption of laser light coming from the NIR spectrometer. In order to solve such problems, this research is focused on an efficient way to identify black plastics. Attenuated Total Reflectance (ATR) Fourier Transform Infrared Radiation (FT-IR) and a Raman spectrometer are used to carry out qualitative and quantitative analysis for the effective as well as efficient classification of black plastic wastes. In this study, to effectively classify the black plastic waste, data processing and Fuzzy Transform (F-Transform) as well as PCA-based Fuzzy Radial Basis Function Neural Networks (FRBFNNs) classifier is proposed. Input variables extracted on a basis of chemical characteristic peaks as well as interval range positioned near the chemical characteristic peaks were exploited as a way to improve the classification performance of the FRBFNN classifier. In order to evaluate the performance of the classifier, a suite of techniques including F-Transform-based as well as Principal Component Analysis (PCA)-based FRBFNNs classifier designed with the aid of Particle Swam Optimization are developed to analyze and classify black plastics.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1300-5
      Issue No: Vol. 49, No. 3 (2019)
       
  • An improved firework algorithm for hardware/software partitioning
    • Authors: Tao Zhang; Qianyu Yue; Xin Zhao; Ganjun Liu
      Pages: 950 - 962
      Abstract: Hardware/software partitioning is a crucial step in the co-design of embedded system. It can not only shorten the R&D cycle, but also improve the performance of the product. In the co-design of embedded system, the hardware/software partitioning algorithm plays the most important role and many heuristic algorithms have been applied to solve this problem. In this paper, we introduce a novel swarm intelligence optimization algorithm called firework algorithm (FWA) and apply it to hardware/software partitioning. In order to improve the optimization accuracy and decrease the time consumed, operators in the conventional FWA are analyzed and their disadvantages are revealed. Then these operators are modified and an improved version of the conventional FWA called improved firework algorithm (IFWA) is proposed. To avoid overwhelming effects, the IFWA provides an innovative calculation of explosion amplitude and spark’s number by setting up dynamic boundaries. Besides, according to grouping and elite strategy, a new selection strategy is put forward to accelerate the convergence speed of the algorithm. Experiments on 8 instances of hardware/software partitioning are conducted in order to illustrate the performance of the proposed algorithm. Experimental results show that the IFWA outperforms significantly the FWA and several other heuristic algorithms in terms of optimization accuracy, time consumed, and convergence speed.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1310-3
      Issue No: Vol. 49, No. 3 (2019)
       
  • Transfer of learning with the co-evolutionary decomposition-based
           algorithm-II: a realization on the bi-level production-distribution
           planning system
    • Authors: Abir Chaabani; Lamjed Ben Said
      Pages: 963 - 982
      Abstract: Bi-Level Optimization Problem (BLOP) is a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem, which has another optimization problem as a constraint. In this way, the evaluation of each upper level solution requires finding an optimal solution to the corresponding lower level problem, which is computationally so expensive. For this reason, most proposed bi-level resolution methods have been restricted to solve the simplest case (linear continuous BLOPs). This fact has attracted the evolutionary computation community to solve such complex problems. Besides, to enhance the search performance of Evolutionary Algorithms (EAs), reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and was demonstrated much promise. Motivated by this observation, we propose in this paper, a memetic version of our proposed Co-evolutionary Decomposition-based Algorithm-II (CODBA-II), that we named M-CODBA-II, to solve combinatorial BLOPs. The main motivation of this paper is to incorporate transfer learning within our recently proposed CODBA-II scheme to make the search process more effective and more efficient. Our proposed hybrid algorithm is investigated on two bi-level production-distribution problems in supply chain management formulated to: (1) Bi-CVRP and (2) Bi-MDVRP. The experimental results reveal a potential advantage of memes incorporation in CODBA-II. Most notably, the results emphasize that transfer learning allows not only accelerating the convergence but also finding better solutions.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1309-9
      Issue No: Vol. 49, No. 3 (2019)
       
  • A computer-aided diagnosis system using Tchebichef features and improved
           grey wolf optimized extreme learning machine
    • Authors: Figlu Mohanty; Suvendu Rup; Bodhisattva Dash; Banshidhar Majhi; M. N. S. Swamy
      Pages: 983 - 1001
      Abstract: Early detection is a key step for effective treatment of breast cancer and computer-aided diagnosis (CAD) is the most common tool used by the medical research community to detect early breast cancer development. Automated and accurate classification of mammogram images is an important criterion for the analysis and interpretation of these images and many methods have been proposed in this direction. In this paper, an improved CAD model is developed to classify the digital mammograms into normal and abnormal, and further, benign and malignant. The proposed model constitutes four different phases, namely, region of interest (ROI) generation, feature extraction, feature reduction, and classification. The proposed model first employs discrete Tchebichef transform (DTT) to extract the features from the ROIs. Subsequently, a technique based on a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) is employed to reduce the dimensions of the feature vector. Next, the reduced features are sent to an extreme learning machine (ELM) for the classification. Here, to obtain a better generalization performance, the hidden node parameters of ELM are optimized through an improved grey wolf optimization-based ELM (IGWO-ELM). To validate the proposed CAD system, different performance metrics such as accuracy, sensitivity, specificity, and area under curve (AUC) are measured using k-fold stratified cross-validation (SCV). Moreover, to eliminate the issue of randomness, 10 independent runs are carried out on SCV. From a detailed analysis of the results, it is observed that the proposed model yields an average accuracy of 100% for MIAS dataset in both normal vs. abnormal, and benign vs. malignant cases. Further, the accuracy achieved for DDSM dataset is 99.50%, and 98.50% for normal vs. abnormal, and benign vs. malignant cases, respectively. The computation time taken by the proposed CAD model for MIAS and DDSM are 1.131 secs and 3.063 secs, respectively. The experimental analysis justifies the effectiveness of the proposed CAD model and as a result, this model can be considered as an effective tool to help the radiologists for better diagnosis.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1294-z
      Issue No: Vol. 49, No. 3 (2019)
       
  • On repeated stackelberg security game with the cooperative human behavior
           model for wildlife protection
    • Authors: Binru Wang; Yuan Zhang; Zhi-Hua Zhou; Sheng Zhong
      Pages: 1002 - 1015
      Abstract: Inspired by successful deployments of Stackelberg Security Game in real life, researchers are working hard to optimize the game models to make them more practical. Recent security game work on wildlife protection makes a step forward by taking the possible cooperation among attackers into consideration. However, it models attackers to have complete rationality, which is not always possible in practice given they are human beings. We aim to tackle attackers’ bounded rationality in the complicated, cooperation-enabled and multi-round security game for wildlife protection. Specifically, we construct a repeated Stackelberg game, and propose a novel adaptive human behavior model for attackers based on it. Despite generating defender’s optimal strategy requires to solve a non-linear and non-convex optimization problem, we are able to propose an efficient algorithm that approximately solve this problem. We perform extensive real-life experiments, and results from over 25,000 game plays show our solution effectively helps the defender to deal with attackers who might cooperate.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1307-y
      Issue No: Vol. 49, No. 3 (2019)
       
  • Improving awareness in early stages of security analysis: A zone partition
           method based on GrC
    • Authors: Hamido Fujita; Angelo Gaeta; Vincenzo Loia; Francesco Orciuoli
      Pages: 1063 - 1077
      Abstract: We present a method based on granular computing to support decision makers in analysing and protecting large-scale infrastructures or urban areas from external attacks by identifying a suitable partition of the infrastructure or the area under analysis. The method works on a very limited set of information relating to the vulnerabilities of components, and probability information regarding how vulnerabilities can impact meaningful partitions. These aspects make the method very useful as a reasoning mechanism to improve awareness and support rapid decision making at early stages of intelligence analysis, when information is scarce and contains a high degree of uncertainty. The results of the case study, which are based on the hypothesis of a terrorist attack on a subway, show that the method provides approximate solutions with the advantages of supporting reasoning at different levels of abstraction and providing simplicity of threat scenario analysis. We also discuss the limitations of the applicability of our approach.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1315-y
      Issue No: Vol. 49, No. 3 (2019)
       
  • Improving lazy decision tree for imbalanced classification by using
           skew-insensitive criteria
    • Authors: Chong Su; Jie Cao
      Pages: 1127 - 1145
      Abstract: Lazy decision tree (LazyDT) constructs a customized decision tree for each test instance, which consists of only a single path from the root to a leaf node. LazyDT has two strengths in comparison with eager decision trees. One is that LazyDT can build shorter decision paths than eager decision trees, and the other is that LazyDT can avoid unnecessary data fragmentation. However, the split criterion used for constructing a customized tree in LazyDT is information gain, which is skew-sensitive. When learning from imbalanced data sets, class imbalance impedes their ability to learn the minority class concept. In this paper, we use Hellinger distance and K-L divergence as split criteria to build two types of lazy decision trees. An experimental framework is performed across a wide range of imbalanced data sets to investigate the effectiveness of our methods when comparing with the other methods including lazy decision tree, C4.5, Hellinger distance based decision tree and support vector machine. In addition, we also use SMOTE to preprocess the highly imbalance data sets in the experiment and evaluate its effectiveness. The experimental results, which contrasted through nonparametric statistical tests, demonstrate that using Hellinger distance and K-L divergence as the split criterion can improve the performances of LazyDT for imbalanced classification effectively.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1314-z
      Issue No: Vol. 49, No. 3 (2019)
       
  • A new block matching algorithm based on stochastic fractal search
    • Authors: Abir Betka; Nadjiba Terki; Abida Toumi; Madina Hamiane; Amina Ourchani
      Pages: 1146 - 1160
      Abstract: Block matching algorithm is the most popular motion estimation technique, due to its simplicity of implementation and effectiveness. However, the algorithm suffers from a long computation time which affects its general performance. In order to achieve faster motion estimation, a new block matching algorithm based on stochastic fractal search, SFS, is proposed in this paper. SFS is a metaheuristic technique used to solve hard optimization problems in minimal time. In this work, two main contributions are presented. The first one consists of computing the motion vectors in a parallel structure as opposed to the other hierarchical metaheuristic block matching algorithms. When the video sequence frame is divided into blocks, a multi-population model of SFS is used to estimate the motion vectors of all blocks simultaneously. As a second contribution, the proposed algorithm is modified in order to enhance the results. In this modified version, four ideas are investigated. The random initialization, usually used in metaheuristics, is replaced by a fixed pattern. The initialized solutions are evaluated using a new fitness function that combines two matching criteria. The considered search space is controlled by a new adaptive window size strategy. A modified version of the fitness approximation method, which is known to reduce computation time but causes some degradation in the estimation accuracy, is proposed to balance between computation time and estimation accuracy. These ideas are evaluated in nine video sequences and the percentage improvement of each idea, in terms of estimation accuracy and computational complexity, is reported. The presented algorithms are then compared with other well-known block matching algorithms. The experimental results indicate that the proposed ideas improve the block matching performance, and show that the proposed algorithm outperforms many state-of-the-art methods.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1312-1
      Issue No: Vol. 49, No. 3 (2019)
       
  • Ensemble based fuzzy weighted extreme learning machine for gene expression
           classification
    • Authors: Yang Wang; Anna Wang; Qing Ai; Haijing Sun
      Pages: 1161 - 1171
      Abstract: Multi-class imbalance is one of the challenging problems in many real-world applications, from medical diagnosis to intrusion detection, etc. Existing methods for gene expression classification usually assume relatively balanced class distribution. However, the assumption is invalid for imbalanced data learning. This paper presents an effective method named EN-FWELM for class imbalance learning. First, based on a fast classifier extreme learning machine (ELM), fuzzy membership of sample is proposed in order to eliminate classification error coming from noise and outlier samples, and balance factor is introduced in combination with sample distribution and sample number associated with class to alleviate the bias against performance caused by imbalanced data. Furthermore, ensemble of ELMs is used for making classification performance more stable and accurate. A number of base ELMs are removed based on dissimilarity measure, and the remaining base ELMs are integrated by majority voting. Finally, experimental results on various gene expression classification and real-world classification demonstrate that the proposed EN-FWELM remarkably outperforms other approaches in the literature.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1322-z
      Issue No: Vol. 49, No. 3 (2019)
       
  • ERR.Rank: An algorithm based on learning to rank for direct optimization
           of Expected Reciprocal Rank
    • Authors: Elham Ghanbari; Azadeh Shakery
      Pages: 1185 - 1199
      Abstract: Learning to rank (LTR) is a machine learning-based ranking technique that constructs a ranking model to sort objects in response to a query, and is used in many applications especially in information retrieval. LTR ranking models are generally evaluated using information retrieval measures. Listwise approaches are among the most important learning to rank algorithms. A subset of listwise approaches try to optimize the evaluation measures. These evaluation measures are dependent only on the document positions in the ranking and are discontinuous and non-convex with respect to the scores of the ranking function. The majority of evaluation measures used by current listwise techniques ignore the relationship between a document at a position and the documents at higher positions. To overcome this problem, we propose a new listwise algorithm, which aims to directly optimize the Expected Reciprocal Rank (ERR) measure. ERR considers the importance of a document at a position to be dependent on the documents ranked higher than this document. Our algorithm uses a probabilistic framework to optimize the expected value of ERR. We use a boosting approach using a gradient descent in order to find the optimal ranking function. The presented algorithm is compared with state of the art algorithms. The results obtained on the ’LETOR 3.0’ standard dataset indicate that the proposed method outperforms the baselines.
      PubDate: 2019-03-01
      DOI: 10.1007/s10489-018-1330-z
      Issue No: Vol. 49, No. 3 (2019)
       
  • Collaborative filtering recommendation algorithm integrating time windows
           and rating predictions
    • Abstract: This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two nonnegative matrixes provide useful probability semantics. The posterior distribution of the real part of the probability model is calculated by the variational inference method. Finally, the preferences for items that users have not rated can be predicted. The user–item rating matrix is supplemented by a preference prediction value, resulting in a dense rating matrix. Finally, time weighting is integrated into the rating matrix to construct the 3D user–item–time model, which gives the recommendation results. According to experiments using open Netflix, MovieLens, and Epinion datasets, the proposed algorithm is superior to several existing recommendation algorithms in terms of rating predictions and recommendation effects.
      PubDate: 2019-03-12
       
  • Multi-view learning with fisher kernel and bi-bagging for imbalanced
           problem
    • Abstract: Existing approaches for handling imbalanced problem are based on the discriminant approaches, while only little attention is dedicated to mining the probability information provided by generative approaches. Moreover, the multi-view learning trains classifier through combining different representations of data for improving the performance of classifier in imbalanced classification. In this paper, a learning framework consisting of fisher kernel and Bi-Bagging is proposed for imbalanced problem. The Fisher kernel is employed to integrate the probability information into the pristine feature of data. Thus, the generated fisher vector contain better discriminatory information. However, the generated fisher vector may lead to high-dimension overfitting. So the dataset represented by the fisher vector is then processed by Bi-Bagging to generate multi-view data and balanced training subsets, which not only reduces the high dimension of generated fisher vector but also promotes the accuracy of minority instances. In one word, the combination of fisher kernel and Bi-Bagging makes use of the probability information in the pristine feature and generates balanced multi-view training subsets with adequate dimension. Therefore, the proposed learning framework is independent of specific models, and the base classifier of the learning framework can be replaced by different linear classifier. Two experimental strategies are implemented to validate the effectiveness of the proposed learning framework for imbalanced datasets on 30 KEEL datasets.
      PubDate: 2019-03-09
       
  • EnSWF: effective features extraction and selection in conjunction with
           ensemble learning methods for document sentiment classification
    • Abstract: With the rise of web 2.0, a huge amount of unstructured data has been generated on regular basis in the form of comments, opinions, etc. This unstructured data contains useful information and can play a significant role in business decision making. In this context, sentiment analysis (SA) is an active research area and has recently attracted the attention of the research community. The aim of SA is to classify the user-generated content into positive and negative class. State-of-the-art techniques for sentiment classification relies on the traditional bag-of-words approaches. Such approaches can be advantageous in terms of simplicity but completely ignore the semantics aspects, the order between words, and also leads to the curse of dimensionality. Researchers have also proposed semantic-based SA techniques in conjunction with word-order employing high order n-grams, part-of-speech (POS) patterns, and dependency relation features. But can every word or phrase of high order n-grams, POS patterns or dependency relation features represent sentiment clue' If incorporated, then what about the dimensionality' In order to tackle and investigate such issues, in this paper, we propose a novel POS and n-gram based ensemble method for SA while considering semantics, sentiment clue, and order between words called EnSWF which is a four phase process. Our main contributions are four-fold (a) Appropriate Feature Extraction: we investigate and validate extracting various appropriate features for sentiment classification. (b) Dimensionality Reduction: We decrease the dimensionality of feature space by selecting the subset of most meaningful and effective features. (c) Ensemble Model: We propose an ensemble learning method for both filter based features selection and classification using simple majority voting technique. (d) Practicality: we authenticate our claim while applying our model on benchmark datasets. We also show that EnSWF out-perform existing techniques in terms of classification accuracy and reduce high dimensional feature space.
      PubDate: 2019-03-09
       
  • A quantum-inspired sentiment representation model for twitter sentiment
           analysis
    • Abstract: Sentiment analysis aims to capture the diverse sentiment information expressed by authors in given natural language texts, and it has been a core research topic in many artificial intelligence areas. The existing machine-learning-based sentiment analysis approaches generally focus on employing popular textual feature representation methods, e.g., term frequency-inverse document frequency (tf-idf), n-gram features, and word embeddings, to construct vector representations of documents. These approaches can model rich syntactic and semantic information, but they largely fail to capture the sentiment information that is central to sentiment analysis. To address this issue, we propose a quantum-inspired sentiment representation (QSR) model. This model can not only represent the semantic content of documents but also capture the sentiment information. Specifically, since adjectives and adverbs are good indicators of subjective expression, this model first extracts sentiment phrases that match the designed sentiment patterns based on adjectives and adverbs. Then, both single words and sentiment phrases in the documents are modeled as a collection of projectors, which are finally encapsulated in density matrices through maximum likelihood estimation. These density matrices successfully integrate the sentiment information into the representations of documents. Extensive experiments are conducted on two widely used Twitter datasets, which are the Obama-McCain Debate (OMD) dataset and the Sentiment140 Twitter dataset. The experimental results show that our model significantly outperforms a number of state-of-the-art baselines and demonstrate the effectiveness of the QSR model for sentiment analysis.
      PubDate: 2019-03-07
       
  • Hybrid generative discriminative approaches based on Multinomial Scaled
           Dirichlet mixture models
    • Abstract: Developing both generative and discriminative techniques for classification has achieved significant progress in the last few years. Considering the capabilities and limitations of both, hybrid generative discriminative approaches have received increasing attention. Our goal is to combine the advantages and desirable properties of generative models, i.e. finite mixture, and the Support Vector Machines (SVMs) as powerful discriminative techniques for modeling count data that appears in many domains in machine learning and computer vision applications. In particular, we select accurate kernels generated from mixtures of Multinomial Scaled Dirichlet distribution and its exponential approximation (EMSD) for support vector machines. We demonstrate the effectiveness and the merits of the proposed framework through challenging real-world applications namely; object recognition and visual scenes classification. Large scale datasets have been considered in the empirical study such as Microsoft MOCR, Fruits-360 and MIT places.
      PubDate: 2019-03-07
       
 
 
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