Neuro-logistic Models Based on Evolutionary Generalized Radial Basis Function for the Microarray Gene Expression Classification Problem
- 3 June 2011
- journal article
- research article
- Published by Springer Nature in Neural Processing Letters
- Vol. 34 (2) , 117-131
Abstract
No abstract availableKeywords
This publication has 26 references indexed in Scilit:
- Evolutionary -Gaussian radial basis function neural networks for multiclassificationNeural Networks, 2011
- MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networksNeurocomputing, 2011
- Evolutionary q-Gaussian Radial Basis Function Neural Network to determine the microbial growth/no growth interface of Staphylococcus aureusApplied Soft Computing, 2011
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Multilogistic regression by means of evolutionary product-unit neural networksNeural Networks, 2008
- Logistic regression using covariates obtained by product-unit neural network modelsPattern Recognition, 2007
- Gene expression profiling predicts clinical outcome of breast cancerNature, 2002
- Prediction of central nervous system embryonal tumour outcome based on gene expressionNature, 2002
- Ridge Estimators in Logistic RegressionJournal of the Royal Statistical Society Series C: Applied Statistics, 1992
- A Comparison of Alternative Tests of Significance for the Problem of $m$ RankingsThe Annals of Mathematical Statistics, 1940