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Kernel-Trick Regression and Classification

  • Received : 2015.02.25
  • Accepted : 2015.03.25
  • Published : 2015.03.31

Abstract

Support vector machine (SVM) is a well known kernel-trick supervised learning tool. This study proposes a working scheme for kernel-trick regression and classification (KtRC) as a SVM alternative. KtRC fits the model on a number of random subsamples and selects the best model. Empirical examples and a simulation study indicate that KtRC's performance is comparable to SVM.

References

  1. Fukumizu, K. (2010). Introduction to Kernel Methods, (written in Japanese), Asakura Publishing, 8-9.
  2. Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning, Second Edition, Springer, 436-437.
  3. Scholkopf, B. and Smola, A. (1998). Learning with Kernels, MIT Press, 118-120.

Cited by

  1. Ensemble approach for improving prediction in kernel regression and classification vol.23, pp.4, 2016, https://doi.org/10.5351/CSAM.2016.23.4.355