Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.Abstract:
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.read more
Citations
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Journal ArticleDOI
Classification of patterns of EEG synchronization for seizure prediction
TL;DR: The authors' best machine learning technique applied to spatio-temporal patterns of EEG synchronization outperformed previous seizure prediction methods on the Freiburg dataset.
Journal ArticleDOI
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
TL;DR: The performance of the GAP-RBF learning algorithm is compared with other well-known sequential learning algorithms like RAN, RANEKF, and MRAN on an artificial problem with uniform input distribution and three real-world nonuniform, higher dimensional benchmark problems.
Proceedings ArticleDOI
Learning on the border: active learning in imbalanced data classification
TL;DR: It is demonstrated that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes and an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset.
Book ChapterDOI
The 2005 PASCAL visual object classes challenge
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool,Moray Allan,Christopher M. Bishop,Olivier Chapelle,Navneet Dalal,Thomas Deselaers,Gyuri Dorkó,Stefan Duffner,J Eichhorn,Jason Farquhar,Mario Fritz,Christophe Garcia,Tom Griffiths,Frédéric Jurie,Daniel Keysers,Markus Koskela,Jorma Laaksonen,Diane Larlus,Bastian Leibe,Hongying Meng,Hermann Ney,Bernt Schiele,Cordelia Schmid,Edgar Seemann,John Shawe-Taylor,Amos Storkey,Sandor Szedmak,Bill Triggs,Ilkay Ulusoy,Ville Viitaniemi,Jianguo Zhang +33 more
TL;DR: The PASCAL Visual Object Classes Challenge (PASCALVOC) as mentioned in this paper was held from February to March 2005 to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects).
Book ChapterDOI
On the Significance of Real‐World Conditions for Material Classification
TL;DR: A first contribution of this paper is to further advance the state-of-the-art by applying Support Vector Machines to this problem and record the best results to date on the CUReT database.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
A Practical Guide to Support Vector Classication
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.