An Improved Hybrid Structure Multi-classification Support Vector Machine
| Autor: | Wang Qiuqiu, Zhang Xiaoyan |
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| Rok vydání: | 2019 |
| Předmět: |
History
Binary tree Sample distance Degree (graph theory) Computer science business.industry Structure (category theory) Pattern recognition Standard deviation Computer Science Applications Education Support vector machine ComputingMethodologies_PATTERNRECOGNITION Binary classification Artificial intelligence business |
| Zdroj: | Journal of Physics: Conference Series. 1187:032096 |
| ISSN: | 1742-6596 1742-6588 |
| Popis: | In order to improve the speed of multi-class support vector machine, based on One-versus-One SVM, the method of combining hierarchical classification is proposed which can reduce the number of classifiers during training and testing, and use the inter-class separation degree, the intra-class sample distance, and the intra-class sample distance standard deviation as the classification measures to divide the subset of binary classification and then form the binary tree structure. Finally, the 1-v-1 training is performed on the subclasses respectively. Experiments show that compared with the traditional 1-v-1 SVM, this method can effectively shorten the time required for classification and reduce the influence of error accumulation of H-SVMs. |
| Databáze: | OpenAIRE |
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