Kernel Matching Reduction Algorithms for Classification

摘要：

Inspired by kernel matching pursuit (KMP) and support vector machines (SVMs),we propose a novel classification algorithm: kernel matching reduction algorithm (KMRA).This method selects all training examples to construct a kernel-based functions dictionary.Then redundant functions are removed iteratively from the dictionary,according to their weights magnitudes,which are determined by linear support vector machines (SVMs).During the reduction process,the parameters of the functions in the dictionary can be adjusted dynamically.Similarities and differences between KMRA and several other machine learning algorithms are also addressed.Experimental results show KMRA can have sparser solutions than SVMs,and can still obtain comparable classification accuracies to SVMs.

关键词： Kernel matching reduction algorithms Kernel matching pursuit Support vector machines Radial basis function neural networks

作者: Jianwu Li Xiaocheng Deng

作者单位: Beijing Key Lab of Intelligent Information,School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 564-571

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）