Foundations of Computational Intelligence

Volume 1: Learning and Approximation

Häftad, Engelska, 2019

Av Aboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos, Witold Pedrycz

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Foundations of Computational Intelligence Volume 1: Learning and Approximation: Theoretical Foundations and Applications Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time.

Produktinformation

Utgivningsdatum2019-03-28
Mått155 x 235 x 23 mm
Vikt622 g
FormatHäftad
SpråkEngelska
SerieStudies in Computational Intelligence
Antal sidor400
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783662568439

Tillhör följande kategorier

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.