An Improved Online Multiple Kernel Classification Algorithm Based on Double Updating Online Learning

摘要：

　　Online multiple kernel classification(OMKC)algorithm is a promising algorithm in machine learning.Because of low error rate and relatively fast training time,it has been sucessfully applied to many real-world problems.However,in the phase of learning a single classifier for a given kernel,the OMKC adopts the perceptron algorithm,which significantly limits the performance of the algorithm.In this paper,we adopts the double updating online learning(DUOL)algorithm to learn the single classifier.Compared to the perceptron algorithm,the DUOL algorithm not only assigns a weight to the misclassified example,but also updates the weight for one of the existing support vectors,which significantly improves the classification performance.Then we use the hedge algorithm to combines these classifiers.The experimental results show that the proposed algorithm is more effective than the OMKC algorithm,the state-of-the-art algorithms,and single kernel learning algorithm.

关键词： online learning multiple kernel learning DUOL OMKC

作者: Yulin Xiao Shangping Zhong

作者单位: College of Mathematics and Computer Science Fuzhou University Fuzhou,350108,China

会议类型: 国际会议

会议名称: 2014 International Conference on Cloud Computing and Internet of Things (CCIOT)(2014年第一届云计算和物联网国际会议)

会议地点: 长春

会议语种:英文

页码: 109-113

在线出版日期: 2014-12-13（万方平台首次上网日期，不代表论文的发表时间）