Efficient model selection for kernel logistic regression

@article{Cawley2004EfficientMS,
  title={Efficient model selection for kernel logistic regression},
  author={Gavin C. Cawley and Nicola L. C. Talbot},
  journal={Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.},
  year={2004},
  volume={2},
  pages={439-442 Vol.2}
}
Kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively reweighted least-squares (IRWLS) algorithm. This approach suggests an approximate leave-one-out cross-validation estimator based on an existing method for exact leave-one-out cross-validation of least-squares models. Results compiled over seven benchmark datasets are presented for kernel logistic regression with model selection procedures based on both conventional k-fold and… CONTINUE READING
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