Foundations of computational intelligence v. 1 Learning and approximation

First volume of a Reference work on the foundations of computational intelligence Devoted to learning and approximation INDICE: From the contents Part I Function Approximation.- Machine Learningand 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.- Part II 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 Feed-forward NeuralNetworks.- Workload Assignment In Production Networks By Multi-Agent Architecture.- Part III 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.

• ISBN: 978-3-642-01081-1
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 300
• Fecha Publicación: 01/05/2009
• Nº Volúmenes: 1
• Idioma: Inglés