Some properties of the Gaussian kernel for one class learning

Authors:
Paul F. Evangelista;Mark J. Embrechts;Boleslaw K. Szymanski
Affiliations:
United States Military Academy, West Point, NY;Rensselaer Polytechnic Institute, Troy, NY;Rensselaer Polytechnic Institute, Troy, NY
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

This paper proposes a novel approach for directly tuning the gaussian kernel matrix for one class learning. The popular gaussian kernel includes a free parameter, σ, that requires tuning typically performed through validation. The value of this parameter impacts model performance significantly. This paper explores an automated method for tuning this kernel based upon a hill climbing optimization of statistics obtained from the kernel matrix.