Two novel deep multi-view support vector machines for multiclass classification
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| Publicat a: | Applied Intelligence vol. 55, no. 2 (Jan 2025), p. 182 |
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| Publicat: |
Springer Nature B.V.
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text - PDF |
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| Resum: | Multi-view classification methods have better generalization performance compared to the single-view classification methods due to the consistency information from multiple views. In recent years, the combination of support vector machine (SVM) and multi-view learning has been widely studied. To improve the robustness of multi-view classification methods, emphasis has shifted to the integration of multi-view classification approaches with fully-connected and convolutional neural networks. A classical deep two-view classification method named deep SVM-2K is a combination of support vector machine with two stage kernel canonical correlation analysis (SVM-2K) and deep learning. However, limitations of deep SVM-2K are that it can not cope with multi-view classification and multiclass classification problems. To address these issues, we propose two novel deep multi-view models named deep multi-view support vector machine (DMVSVM) for multiclass classification. DMVSVM uses the learned features by auto-encoder (AE) or deep neural network (DNN) to train the SVM classifier for each view. The two models then impose some constraints to make the output of the multi-view SVM classifiers as consistent as possible, which used to exploring intrinsic relations. Experiments performed on different real-word datasets show the effectiveness of our proposed approaches. |
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| ISSN: | 0924-669X 1573-7497 |
| DOI: | 10.1007/s10489-024-06126-1 |
| Font: | ABI/INFORM Global |