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Constructive Neural Networks

BuchKartoniert, Paperback
293 Seiten
Englisch
Springererschienen am14.03.2012
This book presents a collection of invited works that consider constructive methods for neural networks, taken primarily from papers presented at a special th session held during the 18 International Conference on Artificial Neural Networks (ICANN 2008) in September 2008 in Prague, Czech Republic.mehr
Verfügbare Formate
BuchGebunden
EUR160,49
BuchKartoniert, Paperback
EUR160,49
E-BookPDF1 - PDF WatermarkE-Book
EUR149,79

Produkt

KlappentextThis book presents a collection of invited works that consider constructive methods for neural networks, taken primarily from papers presented at a special th session held during the 18 International Conference on Artificial Neural Networks (ICANN 2008) in September 2008 in Prague, Czech Republic.
ZusammenfassungConstructive neural networks and other incremental learning algorithms are discussed in this volume as alternatives to methods for assessing adequate architectures. A valuable overview of the field is presented, in addition to useful applications.
Details
ISBN/GTIN978-3-642-26108-4
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2012
Erscheinungsdatum14.03.2012
ReiheStudies in Computational Intelligence
Seiten293 Seiten
SpracheEnglisch
Gewicht464 g
IllustrationenVIII, 293 p.
Artikel-Nr.16928861

Inhalt/Kritik

Inhaltsverzeichnis
Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks.- Efficient Constructive Techniques for Training Switching Neural Networks.- Constructive Neural Network Algorithms That Solve Highly Non-separable Problems.- On Constructing Threshold Networks for Pattern Classification.- Self-Optimizing Neural Network 3.- M-CLANN: Multiclass Concept Lattice-Based Artificial Neural Network.- Constructive Morphological Neural Networks: Some Theoretical Aspects and Experimental Results in Classification.- A Feedforward Constructive Neural Network Algorithm for Multiclass Tasks Based on Linear Separability.- Analysis and Testing of the m-Class RDP Neural Network.- Active Learning Using a Constructive Neural Network Algorithm.- Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks.- A Constructive Neural Network for Evolving a Machine Controller in Real-Time.- Avoiding Prototype Proliferation in Incremental Vector Quantization of Large Heterogeneous Datasets.- Tuning Parameters in Fuzzy Growing Hierarchical Self-Organizing Networks.- Self-Organizing Neural Grove: Efficient Multiple Classifier System with Pruned Self-Generating Neural Trees.mehr