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The relevance vector machine technique for channel equalization application

The relevance vector machine technique for channel equalization application
The relevance vector machine technique for channel equalization application
The recently introduced relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.
1529-1532
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, S., Gunn, S.R. and Harris, C.J. (2001) The relevance vector machine technique for channel equalization application. IEEE Transactions on Neural Networks, 12 (6), 1529-1532.

Record type: Article

Abstract

The recently introduced relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.

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More information

Published date: November 2001
Additional Information: submitted for publication in April 2001, accepted June 2001, to appear Nov. 2001
Organisations: Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 254483
URI: https://eprints.soton.ac.uk/id/eprint/254483
PURE UUID: ed5c49d9-6e6a-42ce-8052-42aef4291639

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Date deposited: 15 Nov 2001
Last modified: 18 Jul 2017 09:52

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Contributors

Author: S. Chen
Author: S.R. Gunn
Author: C.J. Harris

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