Definition
Evolutionary kernel learning stands for using evolutionary algorithms to optimize the kernel function for a kernel-based learning machine.
Motivation and Background
In kernel-based learning algorithms the kernel function determines the scalar product and thereby the metric in the feature space in which the learning algorithm operates. The kernel is usually not adapted by the kernel method itself. Choosing the right kernel function is crucial for the training accuracy and generalization capabilities of the learning machine. It may also influence the runtime and storage complexity during learning and application.
Finding an appropriate kernel is a model selectionproblem. The kernel function is selected from an a priori fixed class. When a parameterized family of kernel functions is considered, kernel adaptation reduces to finding an appropriate parameter vector. In practice, the most frequently used method to determine these values is grid search. In simple grid search the...
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Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(1), 131–159.
Eads, D. R., Hill, D., Davis, S., Perkins, S. J., Ma, J., Porter, R. B., et al. (2002). Genetic algorithms and support vector machines for time series classification. In B. Bosacchi, D. B. Fogel, & J. C. Bezdek (Eds.), Applications and science of neural networks, fuzzy systems, and evolutionary computation V , Proceedings of the SPIE (Vol. 4787) (pp. 74–85). SPIE–The International Society for Optical Engineering. Bellington, WA
Friedrichs, F., & Igel, C. (2005). Evolutionary tuning of multiple SVM parameters. Neurocomputing, 64(C), 107–117.
Fröhlich, H., Chapelle, O., & Schölkopf, B. (2004). Feature selection for support vector machines using genetic algorithms. International Journal on Artificial Intelligence Tools, 13(4), 791–800.
Glasmachers, T., & Igel, C. (2005). Gradient-based adaptation of general gaussian kernels. Neural Computation, 17(10),2099–2105.
Howley, T., & Madden, M. (2005). The genetic kernel support vector machine: Description and evaluation. Artificial Intelligence Review, 24(3), 379–395.
Igel, C. (2005). Multi-objective model selection for support vector machines. In C. A. Coello Coello, E. Zitzler, & A. Hernandez Aguirre (Eds.), Proceedings of the third international conference on evolutionary multi-criterion optimization (EMO 2005), LNCS (Vol. 3410) (pp. 534–546). Berlin: Springer.
Jong, K., Marchiori, E., & van der Vaart, A. (2004). Analysis of proteomic pattern data for cancer detection. In G. R. Raidl, S. Cagnoni, J. Branke, D. W. Corne, R. Drechsler, Y. Jin, et al. (Eds.), Applications of evolutionary computing, LNCS (Vol. 3005, pp. 41–51). Berlin: Springer.
Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C. (2007). Evolutionary optimization of sequence kernels for detection of bacterial gene starts. International Journal of Neural Systems, 17(5), 369–381.
Miller, M. T., Jerebko, A. K., Malley, J. D., & Summers, R. M. (2003). Feature selection for computer-aided polyp detection using genetic algorithms. In A. V. Clough & A. A. Amini (Eds.), Medical imaging 2003: Physiology and function: Methods, systems, and applications, Proceedings of the SPIE (Vol. 5031) (pp. 102–110).
Pang, S., & Kasabov, N. (2004). Inductive vs. transductive inference, global vs. local models: SVM, TSVM, and SVMT for gene expression classification problems. In International joint conference on neural networks (IJCNN 2004) (Vol. 2, pp. 1197–1202). Washington, DC: IEEE Press.
Runarsson, T. P., & Sigurdsson, S. (2004). Asynchronous parallel evolutionary model selection for support vector machines. Neural Information Processing – Letters and Reviews, 3(3), 59–68.
Shi, S. Y. M., Suganthan, P. N., & Deb, K. (2004). Multi-class protein fold recognition using multi-objective evolutionary algorithms. In IEEE symposium on computational intelligence in bioinformatics and computational biology (pp. 61–66). Washington, DC: IEEE Press.
Suttorp, T., & Igel, C. (2006). Multi-objective optimization of support vector machines. In Y. Jin (Ed.), Multi-objective machine learning. Studies in computational intelligence (Vol. 16, pp. 199–220). Berlin: Springer.
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Igel, C. (2011). Evolutionary Kernel Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_284
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DOI: https://doi.org/10.1007/978-0-387-30164-8_284
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
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