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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Proceedings ArticleDOI
Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
Lixin Duan,Dong Xu,Shih-Fu Chang +2 more
TL;DR: This work proposes a new multiple source domain adaptation method called Domain Selection Machine (DSM) for event recognition in consumer videos by leveraging a large number of loosely labeled web images from different sources (e.g., Flickr.com and Photosig.com).
Journal ArticleDOI
Making risk minimization tolerant to label noise
TL;DR: Through extensive empirical studies, it is shown that risk minimization under the 0-1 loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.
Journal ArticleDOI
Top-Down Visual Saliency via Joint CRF and Dictionary Learning
Jimei Yang,Ming-Hsuan Yang +1 more
TL;DR: This paper proposes a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a discriminative dictionary and proposes a max-margin approach to train the dictionary modulated by CRF, and meanwhile a CRF with sparse coding.
Journal ArticleDOI
Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism
TL;DR: A noise-resistant LBP (NRLBP) is proposed to preserve the image local structures in presence of noise and an error-correction mechanism to recover the distorted image patterns is developed.
Journal ArticleDOI
No-Reference Quality Assessment of Screen Content Pictures
TL;DR: A novel blind/no-reference (NR) model for accessing the perceptual quality of screen content pictures with big data learning and delivers computational efficiency and promising performance.
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.