In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. Our formulation exp...
This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (...
We introduce an algorithm that learns gradients from samples in the supervised learning framework. An error analysis is given for the convergence of the gradient estimated by the ...