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
Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples
Yuan Gao,Jiayi Ma,Alan L. Yuille +2 more
TL;DR: In this paper, a semi-supervised sparse representation-based classification method was proposed to deal with the non-linear nuisance variations between labeled and unlabeled samples, where a gallery dictionary consisting of one or more examples of each person and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions and different glasses).
Journal ArticleDOI
Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM
TL;DR: The research has proved that the complexity of SVM (LibSVM) is O(n3) and the time complexity shown that C++ faster than Java, both in training and testing, beside that the data growth will be affect and increase the time of computation.
Journal ArticleDOI
Feature subset selection and feature ranking for multivariate time series
TL;DR: This work proposes a family of novel unsupervised methods for feature subset selection from multivariate time series (MTS) based on common principal component analysis, termed CLeVer, which outperforms RFE, FC, and random selection by up to a factor of two in terms of the classification accuracy, while taking up to 2 orders of magnitude less processing time than RFE and FC.
Proceedings ArticleDOI
Question Classification using Head Words and their Hypernyms
TL;DR: This work proposes head word feature and present two approaches to augment semantic features of such head words using WordNet and proposes a compact yet effective feature set.
Proceedings ArticleDOI
The Interestingness of Images
TL;DR: This work introduces a set of features computationally capturing the three main aspects of visual interestingness and builds an interestingness predictor from them, shown on three datasets with varying context, reflecting the prior knowledge of the viewers.
References
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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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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.