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Xiekai Zhang
Researcher at China University of Mining and Technology
Publications - 6
Citations - 375
Xiekai Zhang is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Support vector machine & Structured support vector machine. The author has an hindex of 6, co-authored 6 publications receiving 260 citations. Previous affiliations of Xiekai Zhang include Chinese Academy of Sciences.
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An overview on semi-supervised support vector machine
TL;DR: The basic theory of S3VM is expounded and discussed in detail, the mainstream model of S 3VM is presented, including transductive support vector machine, Laplacian support Vector machine, S3 VM training via the label mean, S2VM based on cluster kernel and the conclusions are given.
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Twin support vector machine: theory, algorithm and applications
TL;DR: The current state of the theoretical research and practical advances on TWSVM are reported, mainly including least squares twin support vector machine, smooth twin support vectors machine, regularized twin supportvector machine, projection twin support Vector machine, and modified TWS VM on the model selection problem.
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Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification
TL;DR: The overall computational complexity of GWLMBSVM is lower than multi-class WLTSVM classifier, since WLMSVM uses the strategy all-versus-one which is the key idea of multiple birth support vector machine, lower than that of multiple WL TSVM.
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Wavelet twin support vector machines based on glowworm swarm optimization
TL;DR: The proposed wavelet twin support vector machine based on glowworm swarm optimization method is efficient and has high classification accuracy, and the experimental results on benchmark datasets indicate that the proposed approach is efficient.
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An improved multiple birth support vector machine for pattern classification
Xiekai Zhang,Shifei Ding,Yu Xue +2 more
TL;DR: A modified item is added into multiple birth support vector machine to make the variance of the distances from each samples of a given class to their hyperplanes as small as possible and the proposed algorithm is efficient and has good classification performance.