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
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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References
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Journal ArticleDOI
A Simple Decomposition Method for Support Vector Machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Through the design of decomposition methods for bound-constrained SVM formulations, it is demonstrated that the working set selection is not a trivial task and a simple selection is proposed which leads to faster convergences for difficult cases.
Journal ArticleDOI
Building Support Vector Machines with Reduced Classifier Complexity
TL;DR: A primal method that decouples the idea of basis functions from the concept of support vectors and greedily finds a set of kernel basis functions of a specified maximum size to approximate the SVM primal cost function well.
Journal ArticleDOI
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
TL;DR: Convergence of a generalized version of the modified SMO algorithms given by Keerthi et al. for SVM classifier design is proved and the results are extended to modifiedSMO algorithms for solving ν-SVM classifiers problems.
Journal ArticleDOI
A study on SMO-type decomposition methods for support vector machines
TL;DR: The main results include a simple asymptotic convergence proof, a general explanation of the shrinking and caching techniques, and the linear convergence of the methods.
Journal ArticleDOI
Training v -support vector regression: theory and algorithms
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: This work discusses the relation between-support vector regression (-SVR) and v- support vector regression (v-SVR), and focuses on properties that are different from those of C- Support vector classification (C-SVC) andv-supportvector classification (v -SVC).