Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495645 | 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING |
Keywords | Field | DocType |
discriminative training, Minimum Classification Error, MCE, margin, geometric margin | Pattern recognition,Discriminant,Computer science,Computational geometry,Support vector machine,Robustness (computer science),Artificial intelligence,Hidden Markov model,Discriminant function analysis,Margin maximization | Conference |
ISSN | Citations | PageRank |
1520-6149 | 5 | 0.46 |
References | Authors | |
10 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideyuki Watanabe | 1 | 37 | 8.46 |
| Shigeru Katagiri | 2 | 850 | 114.01 |
| Kouta Yamada | 3 | 5 | 0.46 |
| E. McDermott | 4 | 514 | 88.33 |
| Atsushi Nakamura | 5 | 5 | 0.46 |
| Shinji Watanabe | 6 | 1158 | 139.38 |
| Miho Ohsaki | 7 | 195 | 28.23 |