Joel Ratsaby

Learn More
1 INTRODUCTION We investigate the tradeoff between labeled The classical problem of learning a classification rule and unlabeled sample complexities in learning can be stated as follows: patterns from classes " 1 " and a classification rule for a parametric two-class " 2 " (or " states of nature ") appear with probabilities problem. In the problem(More)
1 Introduction One of the main problems in machine learning and statistical inference is selecting an appropriate model by which a set of data can be explained. In the absense of any structured prior information aa to the data generating mechanism, one is often forced to consider a range of models, attempting to select the model which best explains the(More)
The pseudo-dimension of a real-valued function class is an extension of the VC dimension for set-indicator function classes. A class H of finite pseudo-dimension possesses a useful statistical smoothness property. In [10] we introduced a nonlinear approximation width ρ n (F , L q) = inf H n dist(F , H n , L q) which measures the worst-case approximation(More)
In this paper we present a new type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample,(More)