Overview

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition.I n the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

Full Product Details

Author:   Evangelia Miche Tzanakou ,  Richard C. Dorf (University of California, Davis, USA) ,  J. David Irwin (Auburn University, Alabama, USA Auburn University Auburn University Auburn University, Alabama, USA Auburn University Auburn University Auburn University) ,  Timothy Dasey
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Dimensions:   Width: 15.60cm , Height: 2.70cm , Length: 23.40cm
Weight:   0.880kg
ISBN:  

9780849322785

ISBN 10:   0849322782
Pages:   388
Publication Date:   28 December 1999
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

"classifiers-an overview Criteria for optimal classifier design Categorizing the Classifiers Classifiers Neural Networks Comparison of Experimental Results System Performance Assessment Analysis of Prediction Rates from Bootstrapping Assessment ARTIFICIAL NEURAL NETWORKS: DEFINITIONS, METHODS, APPLICATIONS Definitions Training Algorithm Some Applications A SYSTEM FOR HANDWRITTEN DIGIT RECOGNITION Preprocessing of Handwritten Digit Images Zernike Moments (ZM) for Characterization of Image Patterns Dimensionality Reduction Analysis of Prediction Error Rates from Bootstrapping Assessment Summary OTHER TYPES OF FEATURE EXTRACTION METHODS Introduction Wavelets Invariant Moments Entropy Cepstrum Analysis Fractal Dimension Entropy SGLD Texture Features FUZZY NEURAL NETWORKS Pattern Recognition Optimization System Design Clustering APPLICATION TO HANDWRITTEN DIGITS Introduction to Character Recognition Data Collection Results Discussion Summary A UNSUPERVISED NEURAL NETWORK SYSTEM FOR VISUAL EVOKED POTENTIALS Data Collection and Preprocessing System Design Results Discussion CLASSIFICATION OF MAMMOGRAMS USING A MODULAR NEURAL NETWORK Methods and System Overview Modular Neural Networks Neural Network Training Classification Results The Process of Obtaining Results ALOPEX Parameters Generalization Conclusions ""VISUAL OPHTHALMOLOGIST"": AN AUTOMATED SYSTEM FOR CLASSIFICATION OF RETINAL DAMAGE System Overview Modular Neural Networks Applications to Ophthalmology Results Discussion A THREE-DIMENSIONAL NEURAL NETWORK ARCHITECTURE The Neural Network Architecture Simulations Discussion A FEATURE EXTRACTION ALGORITHM USING CONNECTIVITY STRENGTHS AND MOMENT INVARIANTS ALOPEX Algorithms Moment Invariants and ALOPEX Results and Discussion MULTILAYER PERCEPTRONS WITH ALOPEX: 2D-TEMPLATE MATCHING AND VLSI IMPLEMENTATION Multilayer Perceptron and Template Matching VLSI Implementation of ALOPEX IMPLEMENTING NEURAL NETWORKS IN SILICON The Living Neuron Neuromorphic Models Neurological Process Modeling SPEAKER IDENTIFICATION THROUGH WAVELET MULTIRESOLUTION DECOMPOSITION AND ALOPEX Multiresolution Analysis through Wavelet Decomposition Pattern Recognition with ALOPEX Methods Results Discussion FACE RECOGNITION IN ALZHEIMER'S DISEASE: A SIMULATION Methods Results Discussion SELF-LEARNING LAYERED NEURAL NETWORKS Neocognition and Pattern Classification Objectives Methods Study A Study B Summary and Discussion BIOLOGICAL AND MACHINE VISION Distributed Representation The Model A Modified ALOPEX Algorithm Application to Template Matching Brain-to-Computer Link Discussion Each section also has an introduction and references"

Reviews

This book is an excellent source of knowledge of state-of-the-art feature extractionSupervised and unsupervised learning and training schemes are notable findsExciting applications of signal and image analysis and recognitionThis book provides in-depth guidance and inspiring ideas to new applications of signal and image analysis and recognition. --Tonglei Li, Ph.D., Purdue University, School of Pharmacy great efforts have been made in a number of communities to explore solutions to pattern recognition problemsthis book describes their efforts made over ten researchers in the Neuroelectric and Neurocomputing Laboratories at Rutgers University. Along with concise introductory materials in pattern recognition, this volume presents several applications of supervised and unsupervised schemes to the classification of various types of signals and imagesUnlike other books in neural networks, this book gives an emphasis on feature extraction as well, which provides a systematic way to deal with pattern recognition problems in terms of neural networks and computational intelligenceit is worth noting that each chapter contains an extensive bibliography that provides a reliable list of good references. We believe that readers will find this list very useful to understand the materials in the book and cautious beginners in the related fields might benefit from this list as wellhelpful to a broad audience of graduate students, researchers, practicing engineers and professionals in computer and information science, electrical engineering, and biomedical informaticsthis book reflects the long-term continuous endeavors of a research group for conducting innovatory researches, which could provide someuseful hints to those novices in related fieldspioneering volumewelcomed by all interested in the fields of pattern recognition and computational intelligencethe editor's serious attempt to address the aforementioned issue must be welcomed by all interested in the fields of pattern recognition and computational intelligence and, therefore, this book deserves all credit. --Ke Chen, National Laboratory of Machine Perception and The Center for Information Science, Peking University, Beijing, China Promo Copy

This book is an excellent source of knowledge of state-of-the-art feature extraction...Supervised and unsupervised learning and training schemes are notable finds...Exciting applications of signal and image analysis and recognition...This book provides in-depth guidance and inspiring ideas to new applications of signal and image analysis and recognition. --Tonglei Li, Ph.D., Purdue University, School of Pharmacy ...great efforts have been made in a number of communities to explore solutions to pattern recognition problems...this book describes their efforts made over ten researchers in the Neuroelectric and Neurocomputing Laboratories at Rutgers University. Along with concise introductory materials in pattern recognition, this volume presents several applications of supervised and unsupervised schemes to the classification of various types of signals and images...Unlike other books in neural networks, this book gives an emphasis on feature extraction as well, which provides a systematic way to deal with pattern recognition problems in terms of neural networks and computational intelligence...it is worth noting that each chapter contains an extensive bibliography that provides a reliable list of good references. We believe that readers will find this list very useful to understand the materials in the book and cautious beginners in the related fields might benefit from this list as well...helpful to a broad audience of graduate students, researchers, practicing engineers and professionals in computer and information science, electrical engineering, and biomedical informatics...this book reflects the long-term continuous endeavors of a research group for conducting innovatory researches, which could provide some useful hints to those novices in related fields...pioneering volume...welcomed by all interested in the fields of pattern recognition and computational intelligence...the editor's serious attempt to address the aforementioned issue must be welcomed by all interested in the fields of pattern recognition and computational intelligence and, therefore, this book deserves all credit. --Ke Chen, National Laboratory of Machine Perception and The Center for Information Science, Peking University, Beijing, China Promo Copy

Author Information

Evangelia Miche Tzanakou

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