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Adapting Kernels by Variational Approach in SVM

Adapting Kernels by Variational Approach in SVM
Adapting Kernels by Variational Approach in SVM
395-406
Gao, J.B.
5adc3f26-6fe2-4b31-9d1a-d1c64b7eefe0
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Kandola, J.S.
c976459a-d502-4688-b741-334c06796ca8
McKay, B.
da0dd2a7-4d9b-403d-bcc7-7bb301ab37e9
Slaney, J.
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Gao, J.B.
5adc3f26-6fe2-4b31-9d1a-d1c64b7eefe0
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Kandola, J.S.
c976459a-d502-4688-b741-334c06796ca8
McKay, B.
da0dd2a7-4d9b-403d-bcc7-7bb301ab37e9
Slaney, J.
0917024e-b13b-47f6-993a-296eec044e5a

Gao, J.B., Gunn, S.R. and Kandola, J.S. (2002) Adapting Kernels by Variational Approach in SVM. McKay, B. and Slaney, J. (eds.) 15th Australian Joint Conference on Artificial Intelligence, Australia. pp. 395-406 .

Record type: Conference or Workshop Item (Paper)

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More information

Published date: 2002
Venue - Dates: 15th Australian Joint Conference on Artificial Intelligence, Australia, 2002-01-01
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 257843
URI: https://eprints.soton.ac.uk/id/eprint/257843
PURE UUID: 188c2b9b-f3fe-467b-9106-50e4baceccaa

Catalogue record

Date deposited: 29 Nov 2003
Last modified: 16 Jul 2019 22:56

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