Critical Vector Learning to Construct RBF Classifiers

Daming Shi, G.S. Ng, Junbin Gao, D.S. Yeung

Research output: Book chapter/Published conference paperConference paper

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

Sensitivity is initially investigated for the construction of a network prior to its design. Sensitivity analysis applied to network pruning seems particularly useful and valuable when network training involves a large amount of redundant data. This paper proposes a novel learning algorithm for the construction of radial basis function (RBF) classifiers using sensitive vectors (SenV), to which the output is the most sensitive. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results suggests that our proposed methodology outperforms classical RBF classifiers constructed by clustering.
Original languageEnglish
Title of host publication17th International Conference on Pattern Recognition (ICPR'04)
Place of PublicationUSA
PublisherIEEE
Pages359-362
Number of pages4
Volume3
ISBN (Electronic)0769521282
Publication statusPublished - 2004
EventInternational Conference on Pattern Recognition - Cambridge UK, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Conference

ConferenceInternational Conference on Pattern Recognition
CountryUnited Kingdom
Period23/08/0426/08/04

Fingerprint

Classifiers
Neurons
Learning algorithms
Sensitivity analysis

Cite this

Shi, D., Ng, G. S., Gao, J., & Yeung, D. S. (2004). Critical Vector Learning to Construct RBF Classifiers. In 17th International Conference on Pattern Recognition (ICPR'04) (Vol. 3, pp. 359-362). USA: IEEE.
Shi, Daming ; Ng, G.S. ; Gao, Junbin ; Yeung, D.S. / Critical Vector Learning to Construct RBF Classifiers. 17th International Conference on Pattern Recognition (ICPR'04). Vol. 3 USA : IEEE, 2004. pp. 359-362
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Shi, D, Ng, GS, Gao, J & Yeung, DS 2004, Critical Vector Learning to Construct RBF Classifiers. in 17th International Conference on Pattern Recognition (ICPR'04). vol. 3, IEEE, USA, pp. 359-362, International Conference on Pattern Recognition, United Kingdom, 23/08/04.

Critical Vector Learning to Construct RBF Classifiers. / Shi, Daming; Ng, G.S.; Gao, Junbin; Yeung, D.S.

17th International Conference on Pattern Recognition (ICPR'04). Vol. 3 USA : IEEE, 2004. p. 359-362.

Research output: Book chapter/Published conference paperConference paper

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N1 - Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2004. Event dates (773o) = 23-08-2004-26-08-2004; Parent title (773t) = International Conference on Pattern Recognition.

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N2 - Sensitivity is initially investigated for the construction of a network prior to its design. Sensitivity analysis applied to network pruning seems particularly useful and valuable when network training involves a large amount of redundant data. This paper proposes a novel learning algorithm for the construction of radial basis function (RBF) classifiers using sensitive vectors (SenV), to which the output is the most sensitive. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results suggests that our proposed methodology outperforms classical RBF classifiers constructed by clustering.

AB - Sensitivity is initially investigated for the construction of a network prior to its design. Sensitivity analysis applied to network pruning seems particularly useful and valuable when network training involves a large amount of redundant data. This paper proposes a novel learning algorithm for the construction of radial basis function (RBF) classifiers using sensitive vectors (SenV), to which the output is the most sensitive. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results suggests that our proposed methodology outperforms classical RBF classifiers constructed by clustering.

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Shi D, Ng GS, Gao J, Yeung DS. Critical Vector Learning to Construct RBF Classifiers. In 17th International Conference on Pattern Recognition (ICPR'04). Vol. 3. USA: IEEE. 2004. p. 359-362