Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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
Adaptation Regularization: A General Framework for Transfer Learning
TL;DR: A novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model adaptive classifiers in a unified way based on the structural risk minimization principle and the regularization theory, and can significantly outperform state-of-the-art learning methods on several public text and image datasets.
Journal ArticleDOI
Computational Color Constancy: Survey and Experiments
TL;DR: A survey of many recent developments and state-of-the-art methods in computational color constancy, including a taxonomy of existing algorithms, and methods are separated in three groups: static methods, gamut- based methods, and learning-based methods.
Journal ArticleDOI
Probability Models for Open Set Recognition
TL;DR: The general idea of open space risk limiting classification is extended to accommodate non-linear classifiers in a multiclass setting and a new open set recognition model called compact abating probability (CAP), where the probability of class membership decreases in value as points move from known data toward open space.
Proceedings ArticleDOI
Human detection using partial least squares analysis
TL;DR: This paper describes a human detection method that augments widely used edge-based features with texture and color information, providing us with a much richer descriptor set, and is shown to outperform state-of-the-art techniques on three varied datasets.
Proceedings ArticleDOI
Painful data: The UNBC-McMaster shoulder pain expression archive database
TL;DR: A major factor hindering the deployment of a fully functional automatic facial expression detection system is the lack of representative data, so enough data is available to build robust models so high performance can be gained.
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.