Foundations of Computational Intelligence, Volume 1: Learning and Approximation (Paperback, Softcover Repri)

Function Approximation.- Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap.- Automatic Approximation of Expensive Functions with Active Learning.- New Multi-Objective Algorithms for Neural Network Training Applied to Genomic Classification Data.- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy.- Connectionist Learning.- Meta-learning and Neurocomputing - A New Perspective for Computational Intelligence.- Three-Term Fuzzy Back-Propagation.- Entropy Guided Transformation Learning.- Artificial Development.- Robust Training of Artificial Feedforward Neural Networks.- Workload Assignment in Production Networks by Multi Agent Architecture.- Knowledge Representation and Acquisition.- Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning.- A New Implementation for Neural Networks in Fourier-Space.- Learning and Visualization.- Dissimilarity Analysis and Application to Visual Comparisons.- Dynamic Self-Organising Maps: Theory, Methods and Applications.- Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization.