- author = "Bing Wu and Wen-qiong Zhang and Ling Chen and Jia-hong Liang",
- title = "A {GP}-based kernel construction and optimization method for {RVM}",
- booktitle = "The 2nd International Conference on Computer and Automation Engineering (ICCAE 2010)",
- year = "2010",
- volume = "4",
- pages = "419--423",
- address = "Singapore",
- month = "26-28 " # feb,
- keywords = "genetic algorithms, genetic programming, GP-based kernel construction, RVM, SVM, data- driven kernel function construction, optimisation method, relevance vector machine, relevance vector regression, regression analysis, support vector machines",
-
DOI = "
doi:10.1109/ICCAE.2010.5451646",
- size = "5 pages",
- abstract = "Selecting a suitable kernel for relevance vector machine is one of most challenging aspects of successfully using this learning tool. Efficiently automating the search for such a kernel is therefore desirable. This paper proposes a data-driven kernel function construction and optimisation method, which combines genetic programming (GP) and relevance vector regression to evolve an optimal or near-optimal kernel function, named GP-Kernel. The evolved kernel is compared to several widely used kernels on several regression benchmark datasets. Empirical results demonstrate that RVM using such GP-Kernel can outperform or match the best performance of standard kernels.",
-
notes = "College of Mechanical Engineering and Automation,
National University of Defense Technology, Changsha,
China
Also known as \cite{5451646}",
