Improving Efficiency of Multi-Kernel Learning for Support Vector Machines
Support vector machines (SVMs) have be?n successfully applied to classification problems. Practical Issues involve how to determine the right type and suitable hyperparameters of kernel functions. Recently, multiple-kernel learning (MKL) algorithms are developed to handle these Issues by combining different kernels. The weight with each kernel In the combination is obtained through learning. One of the most popular methods Is to learn the weights with semldefinlte programming (SDP). However, the amount of time and space required by this method is demanding. In this study, we reformulate the SDP problem to reduce the time and space requirements. Strategies for reducing the search space in solving the SDP problem are introduced. Experimental results obtained from running on synthetic dalasets and benchmark datasets of UCI and Statlog show that the proposed Approach Improves the efficiency of the SDP method without degrading the performance.
Support vector machines multiple-kernel learning semideflnite programming
Chi-Yuan Yeh Wen-Pin Su Shie-Jue Lee
Department at Electrical Engineering, Natioual Son Yal Sen University, Kaohsiung 804, Taiwan
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3985-3990
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）