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  Subjects -> COMPUTER SCIENCE (Total: 1969 journals)
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    - COMPUTER SCIENCE (1147 journals)
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    - THEORY OF COMPUTING (8 journals)

COMPUTER SCIENCE (1147 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 11)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 67)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 25)
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 35)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 31)
Applied Medical Informatics     Open Access   (Followers: 9)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 8)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 231)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 117)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 12)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 13)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 18)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 47)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 12)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 27)
Computer     Full-text available via subscription   (Followers: 78)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 8)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 13)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 10)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 9)
Computer Science Review     Hybrid Journal   (Followers: 10)

        1 2 3 4 5 6 | Last

Journal Cover Advances in Data Analysis and Classification
  [SJR: 1.113]   [H-I: 14]   [53 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1862-5355 - ISSN (Online) 1862-5347
   Published by Springer-Verlag Homepage  [2329 journals]
  • On visual distances for spectrum-type functional data
    • Authors: A. Cholaquidis; A. Cuevas; R. Fraiman
      Pages: 5 - 24
      Abstract: A functional distance \({\mathbb H}\) , based on the Hausdorff metric between the function hypographs, is proposed for the space \({\mathcal E}\) of non-negative real upper semicontinuous functions on a compact interval. The main goal of the paper is to show that the space \(({\mathcal E},{\mathbb H})\) is particularly suitable in some statistical problems with functional data which involve functions with very wiggly graphs and narrow, sharp peaks. A typical example is given by spectrograms, either obtained by magnetic resonance or by mass spectrometry. On the theoretical side, we show that \(({\mathcal E},{\mathbb H})\) is a complete, separable locally compact space and that the \({\mathbb H}\) -convergence of a sequence of functions implies the convergence of the respective maximum values of these functions. The probabilistic and statistical implications of these results are discussed, in particular regarding the consistency of k-NN classifiers for supervised classification problems with functional data in \({\mathbb H}\) . On the practical side, we provide the results of a small simulation study and check also the performance of our method in two real data problems of supervised classification involving mass spectra.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-015-0217-7
      Issue No: Vol. 11, No. 1 (2017)
       
  • NMF versus ICA for blind source separation
    • Authors: Andri Mirzal
      Pages: 25 - 48
      Abstract: Blind source separation (BSS) is a problem of recovering source signals from signal mixtures without or very limited information about the sources and the mixing process. From literatures, nonnegative matrix factorization (NMF) and independent component analysis (ICA) seem to be the mainstream techniques for solving the BSS problems. Even though the using of NMF and ICA for BSS is well studied, there is still a lack of works that compare the performances of these techniques. Moreover, the nonuniqueness property of NMF is rarely mentioned even though this property actually can make the reconstructed signals vary significantly, and thus introduces the difficulty on how to choose the representative reconstructions from several possible outcomes. In this paper, we compare the performances of NMF and ICA as BSS methods using some standard NMF and ICA algorithms, and point out the difficulty in choosing the representative reconstructions originated from the nonuniqueness property of NMF.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-014-0192-4
      Issue No: Vol. 11, No. 1 (2017)
       
  • Dichotomic lattices and local discretization for Galois lattices
    • Authors: Nathalie Girard; Karell Bertet; Muriel Visani
      Pages: 49 - 77
      Abstract: The present paper deals with supervised classification methods based on Galois lattices and decision trees. Such ordered structures require attributes discretization and it is known that, for decision trees, local discretization improves the classification performance compared with global discretization. While most literature on discretization for Galois lattices relies on global discretization, the presented work introduces a new local discretization algorithm for Galois lattices which hinges on a property of some specific lattices that we introduce as dichotomic lattices. Their properties, co-atomicity and \(\vee \) -complementarity are proved along with their links with decision trees. Finally, some quantitative and qualitative evaluations of the local discretization are proposed.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-015-0225-7
      Issue No: Vol. 11, No. 1 (2017)
       
  • Minimum Class Variance SVM+ for data classification
    • Authors: Wenxin Zhu; Ping Zhong
      Pages: 79 - 96
      Abstract: In this paper, a new Support Vector Machine Plus (SVM+) type model called Minimum Class Variance SVM+ (MCVSVM+) is presented. Similar to SVM+, the proposed model utilizes the group information in the training data. We show that MCVSVM+ has both the advantages of SVM+ and Minimum Class Variance Support Vector Machine (MCVSVM). That is, MCVSVM+ not only considers class distribution characteristics in its optimization problem but also utilizes the additional information (i.e. group information) hidden in the data, in contrast to SVM+ that takes into consideration only the samples that are in the class boundaries. The experimental results demonstrate the validity and advantage of the new model compared with the standard SVM, SVM+ and MCVSVM.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-015-0212-z
      Issue No: Vol. 11, No. 1 (2017)
       
  • A uniform framework for the combination of penalties in generalized
           structured models
    • Authors: Margret-Ruth Oelker; Gerhard Tutz
      Pages: 97 - 120
      Abstract: Penalized estimation has become an established tool for regularization and model selection in regression models. A variety of penalties with specific features are available and effective algorithms for specific penalties have been proposed. But not much is available to fit models with a combination of different penalties. When modeling the rent data of Munich as in our application, various types of predictors call for a combination of a Ridge, a group Lasso and a Lasso-type penalty within one model. We propose to approximate penalties that are (semi-)norms of scalar linear transformations of the coefficient vector in generalized structured models—such that penalties of various kinds can be combined in one model. The approach is very general such that the Lasso, the fused Lasso, the Ridge, the smoothly clipped absolute deviation penalty, the elastic net and many more penalties are embedded. The computation is based on conventional penalized iteratively re-weighted least squares algorithms and hence, easy to implement. New penalties can be incorporated quickly. The approach is extended to penalties with vector based arguments. There are several possibilities to choose the penalty parameter(s). A software implementation is available. Some illustrative examples show promising results.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-015-0205-y
      Issue No: Vol. 11, No. 1 (2017)
       
  • Advances in credit scoring: combining performance and interpretation in
           kernel discriminant analysis
    • Authors: Caterina Liberati; Furio Camillo; Gilbert Saporta
      Pages: 121 - 138
      Abstract: Due to the recent financial turmoil, a discussion in the banking sector about how to accomplish long term success, and how to follow an exhaustive and powerful strategy in credit scoring is being raised up. Recently, the significant theoretical advances in machine learning algorithms have pushed the application of kernel-based classifiers, producing very effective results. Unfortunately, such tools have an inability to provide an explanation, or comprehensible justification, for the solutions they supply. In this paper, we propose a new strategy to model credit scoring data, which exploits, indirectly, the classification power of the kernel machines into an operative field. A reconstruction process of the kernel classifier is performed via linear regression, if all predictors are numerical, or via a general linear model, if some or all predictors are categorical. The loss of performance, due to such approximation, is balanced by better interpretability for the end user, which is able to order, understand and to rank the influence of each category of the variables set in the prediction. An Italian bank case study has been illustrated and discussed; empirical results reveal a promising performance of the introduced strategy.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-015-0213-y
      Issue No: Vol. 11, No. 1 (2017)
       
  • A generalized maximum entropy estimator to simple linear measurement error
           model with a composite indicator
    • Authors: Maurizio Carpita; Enrico Ciavolino
      Pages: 139 - 158
      Abstract: We extend the simple linear measurement error model through the inclusion of a composite indicator by using the generalized maximum entropy estimator. A Monte Carlo simulation study is proposed for comparing the performances of the proposed estimator to his counterpart the ordinary least squares “Adjusted for attenuation”. The two estimators are compared in term of correlation with the true latent variable, standard error and root mean of squared error. Two illustrative case studies are reported in order to discuss the results obtained on the real data set, and relate them to the conclusions drawn via simulation study.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-016-0237-y
      Issue No: Vol. 11, No. 1 (2017)
       
  • Evaluation of the evolution of relationships between topics over time
    • Authors: Wolfgang Gaul; Dominique Vincent
      Pages: 159 - 178
      Abstract: Topics that attract public attention can originate from current events or developments, might be influenced by situations in the past, and often continue to be of interest in the future. When respective information is made available textually, one possibility of detecting such topics of public importance consists in scrutinizing, e.g., appropriate press articles using—given the continual growth of information—text processing techniques enriched by computer routines which examine present-day textual material, check historical publications, find newly emerging topics, and are able to track topic trends over time. Information clustering based on content-(dis)similarity of the underlying textual material and graph-theoretical considerations to deal with the network of relationships between content-similar topics are described and combined in a new approach. Explanatory examples of topic detection and tracking in online news articles illustrate the usefulness of the approach in different situations.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-016-0241-2
      Issue No: Vol. 11, No. 1 (2017)
       
  • Supervised box clustering
    • Authors: Vincenzo Spinelli
      Pages: 179 - 204
      Abstract: In this work we address a technique for effectively clustering points in specific convex sets, called homogeneous boxes, having sides aligned with the coordinate axes (isothetic condition). The proposed clustering approach is based on homogeneity conditions, not according to some distance measure, and, even if it was originally developed in the context of the logical analysis of data, it is now placed inside the framework of Supervised clustering. First, we introduce the basic concepts in box geometry; then, we consider a generalized clustering algorithm based on a class of graphs, called incompatibility graphs. For supervised classification problems, we consider classifiers based on box sets, and compare the overall performances to the accuracy levels of competing methods for a wide range of real data sets. The results show that the proposed method performs comparably with other supervised learning methods in terms of accuracy.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-016-0233-2
      Issue No: Vol. 11, No. 1 (2017)
       
  • Multiple straight-line fitting using a Bayes factor
    • Authors: Carlos Lara-Alvarez; Leonardo Romero; Cuauhtemoc Gomez
      Pages: 205 - 218
      Abstract: This paper introduces a Bayesian approach to solve the problem of fitting multiple straight lines to a set of 2D points. Other approaches use many arbitrary parameters and threshold values, the proposed criterion uses only the parameters of the measurement errors. Models with multiple lines are useful in many applications, this paper analyzes the performance of the new approach to solve a classical problem in robotics: finding a map of lines from laser measurements. Tests show that the Bayesian approach obtains reliable models.
      PubDate: 2017-03-01
      DOI: 10.1007/s11634-016-0236-z
      Issue No: Vol. 11, No. 1 (2017)
       
  • Editorial for issue 2/2017
    • PubDate: 2017-05-17
      DOI: 10.1007/s11634-017-0288-8
       
  • Local generalized quadratic distance metrics: application to the k
           -nearest neighbors classifier
    • Authors: Karim Abou-Moustafa; Frank P. Ferrie
      Abstract: Finding the set of nearest neighbors for a query point of interest appears in a variety of algorithms for machine learning and pattern recognition. Examples include k nearest neighbor classification, information retrieval, case-based reasoning, manifold learning, and nonlinear dimensionality reduction. In this work, we propose a new approach for determining a distance metric from the data for finding such neighboring points. For a query point of interest, our approach learns a generalized quadratic distance (GQD) metric based on the statistical properties in a “small” neighborhood for the point of interest. The locally learned GQD metric captures information such as the density, curvature, and the intrinsic dimensionality for the points falling in this particular neighborhood. Unfortunately, learning the GQD parameters under such a local learning mechanism is a challenging problem with a high computational overhead. To address these challenges, we estimate the GQD parameters using the minimum volume covering ellipsoid (MVCE) for a set of points. The advantage of the MVCE is two-fold. First, the MVCE together with the local learning approach approximate the functionality of a well known robust estimator for covariance matrices. Second, computing the MVCE is a convex optimization problem which, in addition to having a unique global solution, can be efficiently solved using a first order optimization algorithm. We validate our metric learning approach on a large variety of datasets and show that the proposed metric has promising results when compared with five algorithms from the literature for supervised metric learning.
      PubDate: 2017-04-25
      DOI: 10.1007/s11634-017-0286-x
       
  • Ensemble feature selection for high dimensional data: a new method and a
           comparative study
    • Authors: Afef Ben Brahim; Mohamed Limam
      Abstract: The curse of dimensionality is based on the fact that high dimensional data is often difficult to work with. A large number of features can increase the noise of the data and thus the error of a learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the data dimensionality. Different feature selection algorithms may yield feature subsets that can be considered local optima in the space of feature subsets. Ensemble feature selection combines independent feature subsets and might give a better approximation to the optimal subset of features. We propose an ensemble feature selection approach based on feature selectors’ reliability assessment. It aims at providing a unique and stable feature selection without ignoring the predictive accuracy aspect. A classification algorithm is used as an evaluator to assign a confidence to features selected by ensemble members based on their associated classification performance. We compare our proposed approach to several existing techniques and to individual feature selection algorithms. Results show that our approach often improves classification performance and feature selection stability for high dimensional data sets.
      PubDate: 2017-04-24
      DOI: 10.1007/s11634-017-0285-y
       
  • Sparsest factor analysis for clustering variables: a matrix decomposition
           approach
    • Authors: Kohei Adachi; Nickolay T. Trendafilov
      Abstract: We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA.
      PubDate: 2017-04-13
      DOI: 10.1007/s11634-017-0284-z
       
  • Unsupervised classification of children’s bodies using currents
    • Authors: Sonia Barahona; Ximo Gual-Arnau; Maria Victoria Ibáñez; Amelia Simó
      Abstract: Object classification according to their shape and size is of key importance in many scientific fields. This work focuses on the case where the size and shape of an object is characterized by a current. A current is a mathematical object which has been proved relevant to the modeling of geometrical data, like submanifolds, through integration of vector fields along them. As a consequence of the choice of a vector-valued reproducing kernel Hilbert space (RKHS) as a test space for integrating manifolds, it is possible to consider that shapes are embedded in this Hilbert Space. A vector-valued RKHS is a Hilbert space of vector fields; therefore, it is possible to compute a mean of shapes, or to calculate a distance between two manifolds. This embedding enables us to consider size-and-shape clustering algorithms. These algorithms are applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children’s wear.
      PubDate: 2017-03-11
      DOI: 10.1007/s11634-017-0283-0
       
  • Relating brand confusion to ad similarities and brand strengths through
           image data analysis and classification
    • Authors: Daniel Baier; Sarah Frost
      Abstract: Brand confusion occurs when a consumer is exposed to an advertisement (ad) for brand A but believes that it is for brand B. If more consumers are confused in this direction than in the other one (assuming that an ad for B is for A), this asymmetry is a disadvantage for A. Consequently, the confusion potential and structure of ads has to be checked: A sample of consumers is exposed to a sample of ads. For each ad the consumers have to specify their guess about the advertised brand. Then, the collected data are aggregated and analyzed using, e.g., MDS or two-mode clustering. In this paper we compare this approach to a new one where image data analysis and classification is applied: The confusion potential and structure of ads is related to featurewise distances between ads and—to model asymmetric effects—to the strengths of the advertised brands. A sample application for the German beer market is presented, the results are encouraging.
      PubDate: 2017-03-04
      DOI: 10.1007/s11634-017-0282-1
       
  • Cluster-based sparse topical coding for topic mining and document
           clustering
    • Authors: Parvin Ahmadi; Iman Gholampour; Mahmoud Tabandeh
      Abstract: In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents collection. Experimental results on widely-used datasets show that our proposed method significantly outperforms the traditional and other topic model based clustering methods. Our method achieves from 4 to 39% improvement in clustering accuracy and from 2% to more than 44% improvement in normalized mutual information.
      PubDate: 2017-02-28
      DOI: 10.1007/s11634-017-0280-3
       
  • Editorial for issue 1/2017
    • PubDate: 2017-02-22
      DOI: 10.1007/s11634-017-0281-2
       
  • A data driven equivariant approach to constrained Gaussian mixture
           modeling
    • Authors: Roberto Rocci; Stefano Antonio Gattone; Roberto Di Mari
      Abstract: Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods to bypass this obstacle are based on the fact that unboundedness is avoided if the eigenvalues of the covariance matrices are bounded away from zero. This can be done imposing some constraints on the covariance matrices, i.e. by incorporating a priori information on the covariance structure of the mixture components. The present work introduces a constrained approach, where the class conditional covariance matrices are shrunk towards a pre-specified target matrix \(\varvec{\varPsi }.\) Data-driven choices of the matrix \(\varvec{\varPsi },\) when a priori information is not available, and the optimal amount of shrinkage are investigated. Then, constraints based on a data-driven \(\varvec{\varPsi }\) are shown to be equivariant with respect to linear affine transformations, provided that the method used to select the target matrix be also equivariant. The effectiveness of the proposal is evaluated on the basis of a simulation study and an empirical example.
      PubDate: 2017-01-06
      DOI: 10.1007/s11634-016-0279-1
       
  • Probabilistic clustering via Pareto solutions and significance tests
    • Authors: María Teresa Gallegos; Gunter Ritter
      Abstract: The present paper proposes a new strategy for probabilistic (often called model-based) clustering. It is well known that local maxima of mixture likelihoods can be used to partition an underlying data set. However, local maxima are rarely unique. Therefore, it remains to select the reasonable solutions, and in particular the desired one. Credible partitions are usually recognized by separation (and cohesion) of their clusters. We use here the p values provided by the classical tests of Wilks, Hotelling, and Behrens–Fisher to single out those solutions that are well separated by location. It has been shown that reasonable solutions to a clustering problem are related to Pareto points in a plot of scale balance vs. model fit of all local maxima. We briefly review this theory and propose as solutions all well-fitting Pareto points in the set of local maxima separated by location in the above sense. We also design a new iterative, parameter-free cutting plane algorithm for the multivariate Behrens–Fisher problem.
      PubDate: 2016-12-30
      DOI: 10.1007/s11634-016-0278-2
       
 
 
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