Adaptive Least Error Rate Algorithm for Neural Network Classifiers
Chen, S., Mulgrew, B. and Hanzo, L. (2001) Adaptive Least Error Rate Algorithm for Neural Network Classifiers. In, Neural Netwroks for Signal Processing, Falmouth, MA, USA, 10 - 12 Sep 2001. , 223-232.
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Description/Abstract
We consider sample-by-sample adaptive training of two-class neural network classifiers. Specific applications that we have in mind are communication channel equalization and code-division multiple-access (CDMA) multiuser detection. Typically, training of such neural network classifiers is done using some stochastic gradient algorithm that tries to minimize the mean square error (MSE). Since the goal should really be minimizing the error probability, the MSE is a ``wrong'' criterion to use and may lead to a poor performance. We propose a stochastic gradient adaptive minimum error rate (MER) algorithm called the least error rate (LER) for training neural network classifiers.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Presented at 2001 IEEE Workshop on Neural Networks for Signal Processing (Falmouth, MA, USA) , Sept.10-12, 2001 Event Dates: 10-12 September 2001 Organisation: IEEE Signal Processing Society |
| Divisions: | Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control |
| Item ID: | 254285 |
| Date Deposited: | 17 Dec 2003 |
| Last Modified: | 24 May 2013 01:13 |
| Contributors: | Chen, S. (Author) Mulgrew, B. (Author) Hanzo, L. (Author) |
| Date: | September 2001 |
| Additional Information: | Presented at 2001 IEEE Workshop on Neural Networks for Signal Processing (Falmouth, MA, USA) , Sept.10-12, 2001 Event Dates: 10-12 September 2001 Organisation: IEEE Signal Processing Society |
| Status: | Published |
| Further Information: | Google Scholar |
| ISI Citation Count: | 0 |
| URI: | http://eprints.soton.ac.uk/id/eprint/254285 |
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