Abstract. We propose a novel method for addressing the model selection problem in the context of kernel methods. In contrast to existing methods which rely on hold-out testing or t...
This work addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic Algorithms. This problem is also known as the model selection problem. In...
Kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively reweighted least-squares (IRWLS) algorithm. This approach suggest...
We show that linear value-function approximation is equivalent to a form of linear model approximation. We then derive a relationship between the model-approximation error and the...
Ronald Parr, Lihong Li, Gavin Taylor, Christopher ...
In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clus...