Genetic complex multiple kernel for relevance vector regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Wu:2010:ICACC,

• author = "Bing Wu and Wen-Qiong Zhang and Zhi-Wei Hu and Jia-Hong Liang",
• title = "Genetic complex multiple kernel for relevance vector regression",
• booktitle = "2nd International Conference on Advanced Computer Control (ICACC 2010)",
• year = "2010",
• month = "27-29 " # mar,
• volume = "4",
• pages = "217--221",
• abstract = "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 emphasised 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.",
• keywords = "genetic algorithms, genetic programming, classification method, data driven evolutionary approach, genetic complex multiple kernel, genetic complex multiple kernel relevance vector regression, multiple kernel learning, relevance vector machine, sparse Bayesian extension version, support vector machine, Bayes methods, belief networks, learning (artificial intelligence), numerical analysis, pattern classification, regression analysis, support vector machines",
• DOI = "doi:10.1109/ICACC.2010.5486939",
• notes = "Coll. of Mech. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China Also known as \cite{5486939}",

}

Genetic Programming entries for Bing Wu Wen-Qiong Zhang Zhi-Wei Hu Jia-Hong Liang

Citations