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• W2123134294 abstract "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 is an extension of the SOM for mixture density modelling. This paper shows that with an isotropic, density-type kernel function, the kernel SOM is equivalent to a homoscedastic Self-Organising Mixture Network, an entropy-based density estimator. This revelation on the one hand explains that kernelising SOM can improve classification performance by acquiring better probability models of the data; but on the other hand it also explains that the SOM already naturally approximates the kernel method." @default.
• W2123134294 created "2016-06-24" @default.
• W2123134294 creator A5055149475 @default.
• W2123134294 date "2006-07-01" @default.
• W2123134294 modified "2023-10-16" @default.
• W2123134294 title "On the equivalence between kernel self-organising maps and self-organising mixture density networks" @default.
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• W2123134294 doi "https://doi.org/10.1016/j.neunet.2006.05.007" @default.
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