Some Properties of the Gaussian Kernel for One Class Learning

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.

• Type: Conference
• Year: 2007
• Where: ICANN
• Authors: Paul F. Evangelista, Mark J. Embrechts, Boleslaw K. Szymanski
• Keywords: Gaussian Kernel, Gaussian Kernel Matrix, ICANN 2007, Popular Gaussian Kernel