Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence, 1st Edition (Hardback) book cover

Supervised and Unsupervised Pattern Recognition

Feature Extraction and Computational Intelligence, 1st Edition

Edited by Evangelia Miche Tzanakou

CRC Press

392 pages

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Hardback: 9780849322785
pub: 1999-12-28
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Description

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.

In 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.

Reviews

"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

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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

About the Series

Industrial Electronics

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Subject Categories

BISAC Subject Codes/Headings:
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems