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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Proceedings ArticleDOI
Scene Text Recognition using Higher Order Language Priors
TL;DR: A framework is presented that uses a higher order prior computed from an English dictionary to recognize a word, which may or may not be a part of the dictionary, and achieves significant improvement in word recognition accuracies without using a restricted word list.
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A Biologically Inspired System for Action Recognition
TL;DR: The approach builds on recent work on object recognition based on hierarchical feedforward architectures and extends a neurobiological model of motion processing in the visual cortex and finds that sparse features in intermediate stages outperform dense ones and that using a simple feature selection approach leads to an efficient system that performs better with far fewer features.
Journal ArticleDOI
The support vector machine under test
TL;DR: A popular SVM implementation is compared to 16 classification methods and 9 regression methods accessible through the software R by the means of standard performance measures and bias-variance decompositions which showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.
Journal ArticleDOI
Scene Classification Using a Hybrid Generative/Discriminative Approach
TL;DR: This work introduces a novel vocabulary using dense color SIFT descriptors and investigates the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM).
Book ChapterDOI
A User's Guide to Support Vector Machines
Asa Ben-Hur,Jason Weston +1 more
TL;DR: This work provides a basic understanding of the theory behind SVMs and focuses on their use in practice, describing the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.
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
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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.