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      LIBSVM : A library for support vector machines

      1 , 1
      ACM Transactions on Intelligent Systems and Technology
      Association for Computing Machinery (ACM)

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          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.

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          Most cited references24

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          A comparison of methods for multiclass support vector machines.

          Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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            New Support Vector Algorithms

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              Improvements to Platt's SMO Algorithm for SVM Classifier Design

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                Author and article information

                Journal
                ACM Transactions on Intelligent Systems and Technology
                ACM Trans. Intell. Syst. Technol.
                Association for Computing Machinery (ACM)
                2157-6904
                2157-6912
                April 2011
                April 2011
                : 2
                : 3
                : 1-27
                Affiliations
                [1 ]National Taiwan University, Taipei, Taiwan
                Article
                10.1145/1961189.1961199
                f83a2931-ed6a-474d-9716-44aa4cb9e5b9
                © 2011
                Product
                Self URI (article page): https://dl.acm.org/doi/10.1145/1961189.1961199

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