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A stable hyperparameter selection for the Gaussian RBF kernel for discrimination
Author(s) -
Ahn Jeongyoun
Publication year - 2010
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10073
Subject(s) - hyperparameter , kernel (algebra) , gaussian function , computer science , artificial intelligence , pattern recognition (psychology) , radial basis function kernel , radial basis function , machine learning , gaussian , hyperparameter optimization , kernel method , support vector machine , mathematics , artificial neural network , physics , combinatorics , quantum mechanics
Kernel‐based classification methods, for example, support vector machines, map the data into a higher‐dimensional space via a kernel function. In practice, choosing the value of hyperparameter in the kernel function is crucial in order to ensure good performance. We propose a method of selecting the hyperparameter in the Gaussian radial basis function (RBF) kernel by considering the geometry of the embedded feature space. This method is independent of the choice of the discrimination algorithm and also computationally efficient. Its classification performance is competitive with existing methods including cross‐validation. Using simulated and real‐data examples, we show that the proposed method is stable with respect to sampling variability. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 142‐148, 2010