文献类型：会议论文

英文题名：Kernel fisher discriminants and kernel nearest neighbor classifiers: A comparative study for large-scale learning problems

作者：Gao Daqi[1];Li Jie[2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci, Shanghai 200237, Peoples R China

会议论文集：IEEE International Joint Conference on Neural Network

会议日期：JUL 16-21, 2006

会议地点：Vancouver, CANADA

语种：英文

摘要：One of solutions for kernel Fisher discriminants (KFDs) to solve the large-scale learning problems is that all the training samples in a class are covered by multiple hyperspheres one by one, and each sphere should include as many samples from the class as possible. In that way, the KFDs can be carried out in some relatively lower dimensional kernel space. This paper clarifies the fact that a nonlinearly separable dataset in the input space does not certainly become linearly separable in the kernel space. We thus propose a kernel nearest neighbor classification method, Le., a sample is labeled according to the.. urn distance between it and the surfaces of the existing kernels. The experimental results for the letter and the handwritten digit recognitions show that the presented method is quite effective for solving the large-scale learning problems.