Batch map extensions of the kernel-based maximum entropy learning rule

Author

Gautama, Temujin ; Van Hulle, Marc M.

Author_Institution

Lab. voor Neuro en Psychofysiologie, Katholieke Univ. Leuven, Belgium

Volume

17

Issue

2

fYear

2006

fDate

3/1/2006 12:00:00 AM

Firstpage

529

Lastpage

532

Abstract

In this letter, two batch-map extensions are described for the kernel-based maximum entropy learning rule (kMER). In the first, the weights are iteratively set to weighted component-wise medians, while in the second the generalized median is used, enabling kMER to process symbolic data. Simulations are performed to illustrate the extensions.

Keywords

iterative methods; learning (artificial intelligence); maximum entropy methods; self-organising feature maps; batch map extensions; iterative method; kernel-based maximum entropy learning rule; symbolic data processing; weighted component-wise medians; Cooling; Educational programs; Entropy; Iterative algorithms; Neurons; Psychology; Temperature control; Terminology; Training data; Vector quantization; Batch map; generalized median; kMER; Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Entropy; Image Interpretation, Computer-Assisted; Models, Theoretical; Neural Networks (Computer); Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2006.871722

Filename

1603639