Minimum Error Classification with geometric margin control
Hideyuki Watanabe, Shigeru Katagiri, Kouta Yamada, Erik McDermott, Atsushi Nakamura, Shinji Watanabe, Miho Ohsaki · 2010
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.