Genetic Complex Multiple Kernel for Relevance Vector Regression

摘要：

Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy by adapting better the characteristics of the data. This paper presents a data-driven evolutionary approach, called Genetic Complex Multiple Kernel Relevance Vector Regression (GCMK RVR), which combines genetic programming(GP) and relevance vector regression to evolve an optimal or near-optimal complex multiple kernel function. Each GP chromosome is a tree that encodes the mathematical expression of a complex multiple kernel function. Numerical experiments on several benchmark datasets show that the RVR involving this GCMK perform better than not only the widely used simple kernel, Polynomial, Gaussian RBF and Sigmoid kernel, but also the convex linear multiple kernel function.

关键词： Relevance vector regression genetic programming Genetic Complex Multiple Kernel

作者: WuBing Zhang Wen-Qiong Hu Zhi-Wei Liang Jia-Hong

作者单位: College of Mechanical Engineering and Automation National University of Defense Technology Changsha Network Management Centre Second Artillery Arming Beijing China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 217-221

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）