Higher Order Functions for Kernel Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{agapitos:2014:EuroGP,

• author = "Alexandros Agapitos and James McDermott and Michael O'Neill and Ahmed Kattan and Anthony Brabazon",
• title = "Higher Order Functions for Kernel Regression",
• booktitle = "17th European Conference on Genetic Programming",
• year = "2014",
• editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim",
• series = "LNCS",
• volume = "8599",
• publisher = "Springer",
• pages = "1--12",
• address = "Granada, Spain",
• month = "23-25 " # apr,
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-662-44302-6",
• DOI = "doi:10.1007/978-3-662-44303-3_1",
• abstract = "Kernel regression is a well-established nonparametric method, in which the target value of a query point is estimated using a weighted average of the surrounding training examples. The weights are typically obtained by applying a distance-based kernel function, which presupposes the existence of a distance measure. This paper investigates the use of Genetic Programming for the evolution of task-specific distance measures as an alternative to Euclidean distance. Results on seven real-world datasets show that the generalisation performance of the proposed system is superior to that of Euclidean-based kernel regression and standard GP.",
• notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014",

}

Genetic Programming entries for Alexandros Agapitos James McDermott Michael O'Neill Ahmed Kattan Anthony Brabazon

Citations