Cited By
View all- Agapitos AO'neill MKattan ALucas S(2017)Recursion in tree-based genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-016-9277-518:2(149-183)Online publication date: 1-Jun-2017
Higher Order Functions for Kernel Regression
Authors: 
Alexandros Agapitos, James Mcdermott, Michael O'Neill, 
Ahmed Kattan, Anthony BrabazonAuthors Info & Claims
Pages 1 - 12
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.
References
[1]
http://en.wikipedia.org/wiki/Kernelstatistics
[2]
Agapitos, A., Lucas, S.M.: Evolving efficient recursive sorting algorithms. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, July 6-21, pp. 9227---9234. IEEE Press, Vancouver 2006
[3]
Agapitos, A., O'Neill, M., Brabazon, A.: Adaptive distance metrics for nearest neighbour classification based on genetic programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. eds. EuroGP 2013. LNCS, vol. 7831, pp. 1---12. Springer, Heidelberg 2013
[4]
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer 2006
[5]
Frank, A., Asuncion, A.: UCI machine learning repository 2010, http://archive.ics.uci.edu/ml
[6]
Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems 17, pp. 513---520. MIT Press 2004
[7]
Goutte, C., Larsen, J.: Adaptive metric kernel regression. Journal of VLSI Signal Processing 26, 155---167 2000
[8]
Huang, R., Sun, S.: Kernel regression with sparse metric learning. Journal of Intelligent and Fuzzy Systems 244, 775---787 2013
[9]
McDermott, J., Byrne, J., Swafford, J.M., O'Neill, M., Brabazon, A.: Higher-order functions in aesthetic EC encodings. In: 2010 IEEE World Congress on Computational Intelligence, July 18-23, pp. 2816---2823. IEEE Computation Intelligence Society, IEEE Press, Barcelona, Spain 2010
[10]
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd. 2008
[11]
Takeda, H., Farsiu, S., Milanfar, P.: Robust kernel regression for restoration and reconstruction of images from sparse, noisy data. In: Proceeding of the International Conference on Image Processing ICIP, pp. 1257---1260 2006
[12]
Trevor, H., Robert, T., Jerome, F.: The Elements of Statistical Learning, 2nd edn. Springer 2009
[13]
Weinberger, K.Q., Tesauro, G.: Metric learning for kernel regression. In: Eleventh International Conference on Artificial Intelligence and Statistics, pp. 608---615 2007
[14]
Yu, T.: Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions. Genetic Programming and Evolvable Machines 24, 345---380 2001
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Published In
April 2014
245 pages
ISBN:9783662443026
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 23 April 2014
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Cited By
View all- Agapitos AO'neill MKattan ALucas S(2017)Recursion in tree-based genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-016-9277-518:2(149-183)Online publication date: 1-Jun-2017