Two novel deep multi-view support vector machines for multiclass classification
में बचाया:
| में प्रकाशित: | Applied Intelligence vol. 55, no. 2 (Jan 2025), p. 182 |
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| प्रकाशित: |
Springer Nature B.V.
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full Text - PDF |
| टैग: |
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MARC
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| 024 | 7 | |a 10.1007/s10489-024-06126-1 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20250131 | |
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| 245 | 1 | |a Two novel deep multi-view support vector machines for multiclass classification | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Classification | ||
| 653 | |a Machine learning | ||
| 653 | |a Deep learning | ||
| 653 | |a Support vector machines | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Neural networks | ||
| 653 | |a Correlation analysis | ||
| 773 | 0 | |t Applied Intelligence |g vol. 55, no. 2 (Jan 2025), p. 182 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3146696660/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3146696660/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |