This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The mo...
Charles Bergeron, Jed Zaretzki, Curt M. Breneman, ...
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...
Feature selection, as a preprocessing step to machine learning, has been effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improvin...
Although Bayesian model averaging is theoretically the optimal method for combining learned models, it has seen very little use in machine learning. In this paper we study its app...
We report the results of a study on topic spotting in conversational speech. Using a machine learning approach, we build classifiers that accept an audio file of conversational hu...
Kary Myers, Michael J. Kearns, Satinder P. Singh, ...