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Adaptive Least Error Rate Algorithm for Neural Network Classifiers

Adaptive Least Error Rate Algorithm for Neural Network Classifiers
Adaptive Least Error Rate Algorithm for Neural Network Classifiers
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.
223-232
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Mulgrew, B. and Hanzo, L. (2001) Adaptive Least Error Rate Algorithm for Neural Network Classifiers At Neural Networks for Signal Processing, Falmouth, Mass., United States. 10 - 12 Sep 2001. , pp. 223-232.

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published 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
Venue - Dates: Neural Networks for Signal Processing, Falmouth, Mass., United States, 2001-09-10 - 2001-09-12
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 254285
URI: http://eprints.soton.ac.uk/id/eprint/254285
PURE UUID: 7b3f8a9d-08c3-4495-8ff3-628579e6a54d
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 17 Dec 2003
Last modified: 18 Jul 2017 09:53

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Contributors

Author: S. Chen
Author: B. Mulgrew
Author: L. Hanzo ORCID iD

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