Zhang Li, Zhou Weida, Jiao Licheng
Key Laboratory for Radar Signal Processing, Xidian University, Xi'an 710071, China.
IEEE Trans Neural Netw. 2004 Nov;15(6):1424-34. doi: 10.1109/TNN.2004.831161.
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.
本文提出了隐空间支持向量机（HSSVMs）。通过一组隐非线性函数将输入模式映射到高维隐空间，然后将结构风险引入隐空间来构造HSSVMs。此外，HSSVMs中非线性核函数的条件更为宽松，甚至不要求可微性。与支持向量机（SVMs）相比，HSSVMs可以采用更多种类的核函数，因为核函数的正定性质不是必要条件。还分析了HSSVMs在模式识别和回归估计方面的性能。在人工和实际领域的实验证实了我们算法的可行性和有效性。
