Seminar on "Local, Global, and Hybrid Learning" at Institute of Information Science, Academia Sinica, Taiwan on November 4, 2005...

The use of classifiers permeates in various fields of engineering and science disciplines. When constructing classifiers, there is a dichotomy in choosing whether to use local vs. global characteristics of the input data. In this talk, we will describe our work on combining the local and global learning in the Maxi-Min Margin Machine (M^4). M^4 presents a unifying theory that subsumes the Support Vector Machine (SVM), the Minimax Probability Machine (MPM), and the Linear Discriminant Analysis (LDA). While LDA and MPM focus on building the decision plane using global information and SVM focuses on building the decision plane in a local manner, M^4 incorporates these two seemingly different yet complementary characteristics in a unifying framework that achieves good classification accuracy. We will present the formulation of M^4 and also experimental results to show the advantage of our novel model.

 
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