Abstract
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.
| Original language | English |
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| Pages | 67-76 |
| Number of pages | 10 |
| Publication status | Published - 1995 |
| Externally published | Yes |
| Event | Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA Duration: 1995 Aug 31 → 1995 Sept 2 |
Other
| Other | Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) |
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| City | Cambridge, MA, USA |
| Period | 95/8/31 → 95/9/2 |
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering