We examine methods for constructing regression ensembles based on a linear program (LP). The ensemble regression function consists of linear combinations of base hypotheses generat...
Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creatin...
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
We present an algorithm for computing the integral closure of a reduced ring that is finitely generated over a finite field. Leonard and Pellikaan [4] devised an algorithm for c...
Abstract. We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm det...
Abstract. We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification an...
We introduce a new deterministic parallel sorting algorithm based on the regular sampling approach. The algorithm uses only two rounds of regular all-to-all personalized communica...
A fundamental open problem in computational learning theory is whether there is an attribute efficient learning algorithm for the concept class of decision lists (Rivest, 1987; Bl...
We construct a framework which allows an algorithm to turn the distributions produced by some boosting algorithms into polynomially smooth distributions (w.r.t. the PAC oracle...
Estimating overcomplete ICA bases for image windows is a difficult problem. Most algorithms require the estimation of values of the independent components which leads to computatio...