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A comparison of methods for multiclass support vector machines (2002).
| Content Provider | CiteSeerX |
|---|---|
| Author | Lin, Chih-Jen Hsu, Chih-Wei |
| Abstract | 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. |
| File Format | |
| Journal | Ieee Trans. Neural Networks |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Multiclass Problem Binary Classification Support Vector Optimization Problem Several Binary Classifier Support Vector Machine Large Problem Method Dag Method General Need Several Method Ongoing Research Issue Multiclass Classification Acyclic Graph Svm Small Data Set Multiclass Classifier Large-scale Problem Multiclass Support Vector Machine All-together Method Multiclass Svm Practical Use Decomposition Implementation |
| Content Type | Text |
| Resource Type | Article |
