- 期刊
- OpenAccess
Particle Swarm Optimization for Semi-supervised Support Vector Machine
摘要
Semi-supervised Support vector machine has become an increasingly popular tool for machine learning due to its wide applicability. Unlike SVM, their formulation leads to a non-smooth non-convex optimization problem. In 2005, Chapelle and Zien used a Gaussian approximation as a smooth function and presented ∇TSVM. In this paper, we propose a smooth piecewise function and research smooth piecewise semi-supervised support vector machine (SPS^3VM). The approximation performance of the smooth piecewise function is better than the Gaussian approximation function. According to the non-convex character of SPS^3VM, a converging linear particle swarm optimization is first used to train semi-supervised support vector machine. Experimental results illustrate that our proposed algorithm improves ∇TSVM in terms of classification accuracy.
延伸閱讀
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- Zhang, Q. W., & Zhang, F. F. (2020). A Particle Swarm Optimization Algorithm Based on Multi-Subgroup Harmony Search. 電腦學刊, 31(6), 116-126. https://doi.org/10.3966/199115992020123106010
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