Relevance Units Machine for Classification
Classification, a task to assign each input instance to a discrete class label, is a prevailing problem in various areas of study. A great amount of research for developing models for classification has been conducted in machine learning research and recently, kernel-based approaches have drawn considerable attention mainly due to their superiority on generalization and computational efficiency in prediction. In this work, we present a new sparse classification model that integrates the basic theory of a sparse kernel learning model for regression, called relevance units machine, with the generalized linear model. A learning algorithm for the proposed model will be described, followed by experimental analysis comparing its predictive performance on benchmark datasets with that of the support vector machine and relevance vector machine, the two most popular methods for kernel-based classification.
classification sparse kernel model
Mark Menor Kyungim Baek
Department of Information and Computer Sciences University of Hawaii at Manoa Honolulu, Hawaii 96822
国际会议
上海
英文
2272-2276
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）