Kernel Machine for Poisson Regression

Abstract

A kernel machine is proposed as an estimating procedure for the linear and nonlinear Poisson regression, which is based on the penalized negative log-likelihood. The proposed kernel machine provides the estimate of the mean function of the response variable, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation(GCV) function of MSE-type is introduced to determine hyperparameters which affect the performance of the machine. Experimental results are then presented which indicate the performance of the proposed machine.

Keywords

• Canonical Parameter;
• Generalized Cross Validation Function;
• Kernel Machine;
• Penalized Negative Log-Likelihood;
• Poisson Regression