Parallel Separating Hyperplanes for Multi-Class Support Vector Machines
碩士 === 朝陽科技大學 === 資訊工程系 === 102 === The support vector machine (SVM) was originally designed for binary classification, has been extended to deal with multi-class classification problem. It is still an open issue although many studies has been proposed to improve the performance of multi-class class...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2014
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| Online Access: | http://ndltd.ncl.edu.tw/handle/86587143114325673189 |
| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系 === 102 === The support vector machine (SVM) was originally designed for binary classification, has been extended to deal with multi-class classification problem. It is still an open issue although many studies has been proposed to improve the performance of multi-class classification. This paper presents a novel multi-class support vector machine to improve the performance of applying support vector machine on multi-class problem. In the novel scheme parallel hyperplanes are used as the separating hyperplanes to classify patterns. This paper proved that the transformed functions which the patterns are mapped from the data space into the feature space and can be classified by parallel hyperplanes are existed. This paper presents a class degree decision algorithms to predict the class locations in feature space. Experimental results show that our proposed scheme outperform others.
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