Multi-class support vector machines based on the mahalanobis distance
| Autor: | Chang-Lun Zhang, Yan-Fei Gao, Heng-You Wang |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Computer Science::Machine Learning
Mahalanobis distance Contextual image classification Structured support vector machine business.industry Feature vector Pattern recognition Euclidean distance Support vector machine Relevance vector machine Statistics::Machine Learning ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Radial basis function kernel Artificial intelligence business Mathematics |
| Zdroj: | ICMLC |
| DOI: | 10.1109/icmlc.2011.6016824 |
| Popis: | In the last decade, Support vector machine (SVM) has been deeply investigated and it is often used in Hilbert space by the measure of Euclidean distance. In this paper, we present the SVM with mahalanobis distance, and the details of how to compute the mahalanobis distance in the input and the feature space are described. Finally, we apply it to the image classification and compare the results of them. By this, we obtain a sound conclusion. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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