In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise...
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competit...
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
This paper describes the current state of RUgle, a system for classifying and indexing papers made available on the World Wide Web, in a domain-independent and universal manner. B...
Often the most expensive and time-consuming task in building a pattern recognition system is col lecting and accurately labeling training and testing data. In this paper, we exp...