Hidden Space Support Vector Machines

作者：Li Zhang, Weiping Zhou, Licheng Jiao · 发表于：IEEE Transactions on Neural Networks · 年份：2004 · DOI：10.1109/tnn.2004.831161 · 被引用次数：44 · 研究领域：Face and Expression Recognition、Neural Networks and Applications、Advanced Algorithms and Applications

Hidden space support vector machines (HSSVMs) are presented in this paper. The input patterns are mapped into a high-dimensional hidden space by a set of hidden nonlinear functions and then the structural risk is introduced into the hidden space to construct HSSVMs. Moreover, the conditions for the nonlinear kernel function in HSSVMs are more relaxed, and even differentiability is not required. Compared with support vector machines (SVMs), HSSVMs can adopt more kinds of kernel functions because the positive definite property of the kernel function is not a necessary condition. The performance of HSSVMs for pattern recognition and regression estimation is also analyzed. Experiments on artificial and real-world domains confirm the feasibility and the validity of our algorithms.