1. Minimum Error Classification with geometric margin control
- Author
-
Erik McDermott, Miho Ohsaki, Kouta Yamada, Shigeru Katagiri, Shinji Watanabe, Atsushi Nakamura, and Hideyuki Watanabe
- Subjects
Support vector machine ,ComputingMethodologies_PATTERNRECOGNITION ,Discriminant ,Discriminant function analysis ,Computer science ,Robustness (computer science) ,business.industry ,Pattern recognition ,Artificial intelligence ,Hidden Markov model ,business - Abstract
Minimum Classification Error (MCE) training, which can be used to achieve minimum error classification of various types of patterns, has attracted a great deal of attention. However, to increase classification robustness, a conventional MCE framework has no practical optimization procedures like geometric margin maximization in Support Vector Machine (SVM). To realize high robustness in a wide range of classification tasks, we derive the geometric margin for a general class of discriminant functions and develop a new MCE training method that increases the geometric margin value. We also experimentally demonstrate the effectiveness of our new method using prototype-based classifiers.
- Published
- 2010
- Full Text
- View/download PDF