Wednesday, 15:15 - 15:40 h, Room: MA 004


Akiko Takeda
Robust optimization-based classification method

Coauthors: Takafumi Kanamori, Hiroyuki Mitsugi


The goal of binary classification is to predict the class (e.g., +1 or -1) to which new observations belong, where the identity of the class is unknown, on the basis of a training set of data containing observations whose class is known. A wide variety of machine learning algorithms such as support vector machine (SVM), minimax probability machine (MPM), Fisher discriminant analysis (FDA), exist for binary classification. The purpose of this paper is to provide a unified classification model that includes the above models through a robust optimization approach. This unified model has several benefits. One is that the extensions and improvements intended for SVM become applicable to MPM and FDA, and vice versa. Another benefit is to provide theoretical results to above learning methods at once by dealing with the unified model. We also propose a non-convex optimization algorithm that can be applied to non-convex variants of existing learning methods and show promising numerical results.


Talk 1 of the contributed session Wed.3.MA 004
"Applications of robust optimization V" [...]
Cluster 20
"Robust optimization" [...]


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