Embedded Classification Kernel Using Som Clustering and Mixture of Experts
نویسندگان
چکیده
-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. Key words---Supervised mixture models, Mixture of Experts, and Self organizing map
منابع مشابه
On the equivalence between kernel self-organising maps and self-organising mixture density networks
The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network ...
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