The nature of statistical learning theory
The nature of statistical learning theory
Computational Statistics & Data Analysis
Neural networks and logistic regression: Part I
Computational Statistics & Data Analysis
Neural networks and logistic regression: Part II
Computational Statistics & Data Analysis
A hybrid neural network model in handwritten word recognition
Neural Networks
Training Product Unit Neural Networks with Genetic Algorithms
IEEE Expert: Intelligent Systems and Their Applications
Extracting regression rules from neural networks
Neural Networks
Multilayer statistical classifiers
Computational Statistics & Data Analysis
On the quality of ART1 text clustering
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Machine Learning
Machine Learning
ASR for emotional speech: Clarifying the issues and enhancing performance
Neural Networks - Special issue: Emotion and brain
Evolutionary product unit based neural networks for regression
Neural Networks
Self-organizing neural networks to support the discovery of DNA-binding motifs
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Learning polynomial feedforward neural networks by genetic programming and backpropagation
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Computers and Electronics in Agriculture
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
MultiLogistic regression using initial and radial basis function covariates
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Hybrid artificial neural networks: models, algorithms and data
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Label dependent evolutionary feature weighting for remote sensing data
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Improvement of accuracy in a sound synthesis method using Evolutionary Product Unit Networks
Expert Systems with Applications: An International Journal
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We propose a multilogistic regression model based on the combination of linear and product-unit models, where the product-unit nonlinear functions are constructed with the product of the inputs raised to arbitrary powers. The estimation of the coefficients of the model is carried out in two phases. First, the number of product-unit basis functions and the exponents' vector are determined by means of an evolutionary neural network algorithm. Afterwards, a standard maximum likelihood optimization method determines the rest of the coefficients in the new space given by the initial variables and the product-unit basis functions previously estimated. We compare the performance of our approach with the logistic regression built on the initial variables and several learning classification techniques. The statistical test carried out on twelve benchmark datasets shows that the proposed model is competitive in terms of the accuracy of the classifier.