Embedded classification kernel using SOM clustering and mixture of experts

Authors:
S. Meenakshisundaram;S. S. Dlay;W. L. Woo
Affiliations:
School of Electrical Electronic and Computer Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, United Kingdom;School of Electrical Electronic and Computer Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, United Kingdom;School of Electrical Electronic and Computer Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, United Kingdom
Venue:
ICECS'05 Proceedings of the 4th WSEAS international conference on Electronics, control and signal processing
Year:
2005

Citing 3
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Abstract

In this paper, we introduce a new classification kernel by embedding self organized map (SOM) clustering with mixture of radial basis function (RBF) networks. The model's efficacy is demonstrated in solving a multi-class TIMIT speech recognition problem where the kernel is used to learn the multidimensional cepstral feature vectors to estimate their posterior class probabilities. The tests results have shown that this model provides a better alternative to the state of the art models achieving a significant improvement in error performance, reduction in complexity and gain in training time.