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» Variable selection using neural-network models
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CORR
2007
Springer
94views Education» more  CORR 2007»
14 years 9 months ago
Statistical tools to assess the reliability of self-organizing maps
Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramat...
Eric de Bodt, Marie Cottrell, Michel Verleysen
WSTST
2005
Springer
15 years 3 months ago
Hybrid Neurocomputing for Breast Cancer Detection
Breast cancer is one of the major tumor related cause of death in women. Various artificial intelligence techniques have been used to improve the diagnoses procedures and to aid t...
Yuehui Chen, Ajith Abraham, Bo Yang
ICCS
2003
Springer
15 years 2 months ago
Self-Organizing Hybrid Neurofuzzy Networks
Abstract. We introduce a concept of self-organizing Hybrid Neurofuzzy Networks (HNFN), a hybrid modeling architecture combining neurofuzzy (NF) and polynomial neural networks(PNN)....
Sung-Kwun Oh, Su-Chong Joo, Chang-Won Jeong, Hyun-...
75
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ICANN
2009
Springer
15 years 4 months ago
Using Kernel Basis with Relevance Vector Machine for Feature Selection
This paper presents an application of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works...
Frederic Suard, David Mercier
PRL
1998
132views more  PRL 1998»
14 years 9 months ago
Unsupervised feature selection using a neuro-fuzzy approach
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a ¯exible membersh...
Jayanta Basak, Rajat K. De, Sankar K. Pal