Neurocomputing

Learning, Architectures & Modeling

By: Elizabeth T Mueller (Editor)

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Hardcover | 4 October 2012

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Hardcover
219 Pages

Dimensions(cm)
25.7 x 18.4 x 1.9

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In this book, the authors present topical research in the study of the architectures and modelling in neurocomputing. Topics discussed include a brain-computer interface for analysing investment behaviour and market stability; neural-based image segmentation architecture with execution on a GPU; EEG montages for the diagnosis of Alzheimer's disease; design and training of neural architectures using extreme learning machines; and, the systematic comparison of single and multiple hidden-layer neural networks.

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Preface	p. vii
Intelligent Market: A Brain-Computer Interface for Analyzing Investment Behavior and Market Stability	p. 1
Neural-Based Image Segmentation Architecture with Execution on a GPU	p. 33
Learning for Combining Fingerprint Matchers: A Case Study FVC-onGoing	p. 67
Comparison of EEG Montages for Diagnosis of Alzheimer's Disease Using Spectral Features and Support Vector Machines	p. 83
Portfolio Optimization with Dimension Reduction Techniques: A Comprehensive Simulation Study	p. 93
Design and Training of Neural Architectures using Extreme Learning Machines	p. 119
Systematic Comparisons of Single-and Multiple-Hidden-Layer Neural Networks	p. 147
Learning Multiple ICA Modules for Distributed and Factorial Neural Representations	p. 163
Context-Sensitive Recurrent Filters for Visual Motion Analysis	p. 189
Index	p. 213
Table of Contents provided by Ingram. All Rights Reserved.

Neurocomputing

Learning, Architectures & Modeling

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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