Methodology - New basis functions for artificial neural networks
Descripción
Desarrollo y estudio de nuevas funciones de base para los distintos nodos de las Redes Neuronales Artificiales.
Publicaciones
- Generalised Gaussian Radial Basis Function Neural Networks
- Parameter estimation of q-Gaussian Radial Basis Functions Neural Networks with a Hybrid Algorithm for Binary Classi fication
- Evolutionary Generalized Radial Basis Function Neural Networks for improving prediction accuracy in gene classification using feature selection
- Neuro-logistic models based on Evolutionary Generalized Radial Basis Function for the microarray gene expression classification problem
- MELM-GRBF: A modified version of the Extreme Learning Machine for Generalized Radial Basis Function Neural Networks
- Combining Evolutionary Generalized Radial Basis Function and Logistic Regression Methods for Classification
- Evolutionary q-Gaussian Radial Basis Functions for Binary-Classification
- Evolutionary q-Gaussian Radial Basis Functions for improving prediction accuracy of gene classification using feature selection
- Aprendizaje hibrido de redes neuronales q-Gaussianas en clasificación binaria
- Classification by Evolutionary Generalized Radial Basis Functions
- Combined Projection and Kernel Basis Functions for Classification in Evolutionary Neural Networks
- Classification by Evolutionary Generalized Radial Basis Functions
- Multilogistic Regression by Product Units
- Multilogistic Regression by means of Evolutionary Product-Unit Neural Networks
- Evolutionary Product-Unit Neural Networks Classifiers
- Logistic Regression using covariates obtained by Product Unit Neural Networks models
- Hybrid Evolutionary Algorithm with Product-Unit Neural Networks for Classification
- Distribution of the Search of Evolutionary Product Unit Neural Networks for Classification
- Distribución de Modelos de Redes Neuronales Evolutivas de Unidades Producto para Clasificación
- Evolutionary Product Unit based Neural Networks for Regression
