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Improving SVM Classification with Imbalance Data Set.

Z. Zeng, и J. Gao. ICONIP (1), том 5863 из Lecture Notes in Computer Science, стр. 389-398. Springer, (2009)

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A Hybrid Method for Speeding SVM Training.Z. Zeng, J. Gao, и H. Guo. NGITS, том 4032 из Lecture Notes in Computer Science, стр. 312-320. Springer, (2006)Unsupervised feature analysis with sparse adaptive learning.X. Wang, R. Chen, C. Hong, и Z. qiang Zeng. Pattern Recognit. Lett., (2018)Fast training Support Vector Machines using parallel sequential minimal optimization.Z. Zeng, H. Yu, H. Xu, Y. Xie, и J. Gao. ISKE, стр. 997-1001. IEEE, (2008)Simplified Support Vector Machines Via Kernel-Based Clustering.Z. Zeng, J. Gao, и H. Guo. Australian Conference on Artificial Intelligence, том 4304 из Lecture Notes in Computer Science, стр. 1189-1195. Springer, (2006)Improving SVM Classification with Imbalance Data Set.Z. Zeng, и J. Gao. ICONIP (1), том 5863 из Lecture Notes in Computer Science, стр. 389-398. Springer, (2009)A geometric approach to train SVM on very large data sets.Z. Zeng, H. Xu, Y. Xie, и J. Gao. ISKE, стр. 991-996. IEEE, (2008)