Inspired by the underlying relationship between classification capability and the mutual information, in this paper, we first establish a quantitative model to describe the inform...
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
We propose a novel cost-efficient approach to threshold selection for binary web-page classification problems with imbalanced class distributions. In many binary-classification ta...
We consider the problem of learning classifiers in structured domains, where some objects have a subset of features that are inherently absent due to complex relationships between...
Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbe...
We propose a method for the classification of matrices. We use a linear classifier with a novel regularization scheme based on the spectral 1-norm of its coefficient matrix. The s...