抄録
Recently, a minimum classification error training algorithm has been proposed for minimizing the misclassification probability based on a given set of training samples using a generalized probabilistic descent method. This algorithm is a type of discriminative learning algorithm, but it approaches the objective of minimum classification error in a more direct manner than the conventional discriminative training algorithms. We apply this algorithm for simultaneous design of feature extractor and pattern classifier, and demonstrate some of its properties and advantages.
| 本文言語 | English |
|---|---|
| ページ | 67-76 |
| ページ数 | 10 |
| 出版ステータス | Published - 1995 |
| 外部発表 | はい |
| イベント | Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA 継続期間: 1995 8月 31 → 1995 9月 2 |
Other
| Other | Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) |
|---|---|
| City | Cambridge, MA, USA |
| Period | 95/8/31 → 95/9/2 |
ASJC Scopus subject areas
- 信号処理
- ソフトウェア
- 電子工学および電気工学