We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discre...
We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, find those that bind to a target molecule in as few iterations o...
Several authors have theoretically determined distribution-free bounds on sample complexity. Formulas based on several learning paradigms have been presented. However, little is kn...
In this work, we present a new semantic language modeling approach to model news stories in the Topic Detection and Tracking (TDT) task. In the new approach, we build a unigram la...
Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are frequently used to overcome these difficulties. We ...