Diferencia entre revisiones de «Relevance Dendritic Computing: codes and examples»
De Grupo de Inteligencia Computacional (GIC)
Sin resumen de edición |
Sin resumen de edición |
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| Línea 2: | Línea 2: | ||
:Download the MATLAB code for Relevance Dendritic Computing here: | :Download the MATLAB code for Relevance Dendritic Computing here: | ||
[[Media:SLKN_code.zip | Download ]] | :[[Media:SLKN_code.zip | Download ]] | ||
:Datasets avalaible in: | :Datasets avalaible in: | ||
[[http://www.fml.tuebingen.mpg.de/Members/raetsch/benchmark/?searchterm=benchmark | Benchmarks datasets]] | :[[http://www.fml.tuebingen.mpg.de/Members/raetsch/benchmark/?searchterm=benchmark | Benchmarks datasets]] | ||
: The details of the method are given in the paper | : The details of the method are given in the paper | ||
Revisión del 21:50 9 dic 2011
This page is devoted to publishing code for Relevance Dendritic Computing and some execution examples.
- Download the MATLAB code for Relevance Dendritic Computing here:
- Download
- Datasets avalaible in:
- [| Benchmarks datasets]
- The details of the method are given in the paper
Learning Parsimonious Dendritic Classifiers M. Graña, and A.I. Gonzalez, Neurocomputing (submitted for publication).
- Some results obtained. Figures shown the distribution of class 1 (blue dot region) obtained by training on the (a) XOR, (b) Gaussians centered at the XOR points, (c) the synthetic data used by Tipping and (d) synthetic ring data.
(a) 
(b) 
(c) 
(d) Copyright 2011 Grupo Inteligencia Computacional, Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU).
