Robust Pattern Recognition Based On Fuzzy Objective Functions

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Robust Pattern Recognition Based On Fuzzy Objective Functions

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Author : 楊泰寧
language : en
Publisher:
Release Date : 2000

Robust Pattern Recognition Based On Fuzzy Objective Functions written by 楊泰寧 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.

Pattern Recognition With Fuzzy Objective Function Algorithms

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Author : James C. Bezdek
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-13

Pattern Recognition With Fuzzy Objective Function Algorithms written by James C. Bezdek and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-13 with Mathematics categories.

The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.

Pattern Recognition With Fuzzy Objective Function

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Author : James C. Bezdek
language : en
Publisher:
Release Date : 1981

Pattern Recognition With Fuzzy Objective Function written by James C. Bezdek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with Cluster analysis categories.

Fuzzy Models And Algorithms For Pattern Recognition And Image Processing

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Author : James C. Bezdek
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-28

Fuzzy Models And Algorithms For Pattern Recognition And Image Processing written by James C. Bezdek and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-28 with Computers categories.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.

Fuzzy Algorithms With Applications To Image Processing And Pattern Recognition

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Author : Zheru Chi
language : en
Publisher: World Scientific
Release Date : 1996-10-04

Fuzzy Algorithms With Applications To Image Processing And Pattern Recognition written by Zheru Chi and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-10-04 with Computers categories.

Contents:Introduction:Basic Concepts of Fuzzy SetsFuzzy RelationsFuzzy Models for Image Processing and Pattern RecognitionMembership Functions:IntroductionHeuristic SelectionsClustering ApproachesTuning of Membership FunctionsConcluding RemarksOptimal Image Thresholding:IntroductionThreshold Selection Based on Statistical Decision TheoryNon-fuzzy Thresholding AlgorithmsFuzzy Thresholding AlgorithmUnified Formulation of Three Thresholding AlgorithmsMultilevel ThresholdingApplicationsConcluding RemarksFuzzy Clustering:IntroductionC-Means AlgorithmFuzzy C-Means AlgorithmComparison between Hard and Fuzzy Clustering AlgorithmsCluster ValidityApplicationsConcluding RemarksLine Pattern Matching:IntroductionSimilarity Measures between Line SegmentsBasic Matching AlgorithmDealing with Noisy PatternsDealing with Rotated PatternsApplicationsConcluding RemarksFuzzy Rule-based Systems:IntroductionLearning from ExamplesDecision Tree ApproachFuzzy Aggregation Network ApproachMinimization of Fuzzy RulesDefuzzification and OptimizationApplicationsConcluding RemarksCombined Classifiers:IntroductionVoting SchemesMaximum Posteriori ProbabilityMultilayer Perceptron ApproachFuzzy Measures and Fuzzy IntegralsApplicationsConcluding Remarks Readership: Engineers and computer scientists. keywords:

Pattern Recognition Using Robust Discrimination And Fuzzy Set Theoretic Preprocessing

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Author :
language : en
Publisher:
Release Date : 1907

Pattern Recognition Using Robust Discrimination And Fuzzy Set Theoretic Preprocessing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1907 with categories.

Classification is the empirical process of creating a mapping from individual patterns to a set of classes and its subsequent use in predicting the classes to which new patterns belong. Tremendous energies have been expended in developing systems for the creation of the mapping component. Less effort has been devoted to the nature and analysis of the data component, namely, strategies that transform the data in order to simplify, in some sense, the classification process. The purpose of this thesis is to redress somewhat this imbalance by introducing two novel preprocessing methodologies. Fuzzy interruptible encoding determines the respective degrees to which a feature belongs to a collection of fuzzy sets and subsequently using these membership grades in place of the original feature. Burnishing tarnished gold standards compensates for the possible imprecision of a well-established reference test by adjusting, if necessary, the class labels in the design set while maintaining the test's vital discriminatory power. The methodologies were applied to several synthetic data sets as well as biomedical spectra acquired from magnetic resonance and infrared spectrometers. Both fuzzy encoding and burnishing consistently improved the discriminatory power of the underlying classifiers. They are insensitive to outliers and often reduce the training time for iterative classifiers such as the multi-layer perceptron. With the latter, reclassification only occurs for data within the design set; outliers within the test set are flagged but not altered. Therefore, the accepted gold standard is left in a pristine state sullied only by its original tarnish.

Pattern Recognition Using Robust Discrimination And Fuzzy Set Theoretic Preprocessing

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Author : Nicolino John Pizzi
language : en
Publisher:
Release Date : 1997

Pattern Recognition Using Robust Discrimination And Fuzzy Set Theoretic Preprocessing written by Nicolino John Pizzi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.

Pattern Recognition Using Robust Discrimination And Fuzzy Set Theoretic Preprocessing

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Author : Nicolino John Pizzi
language : en
Publisher:
Release Date : 1997

Pattern Recognition Using Robust Discrimination And Fuzzy Set Theoretic Preprocessing written by Nicolino John Pizzi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.

Pattern Recognition

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Author : Sankar K. Pal
language : en
Publisher: World Scientific
Release Date : 2001

Pattern Recognition written by Sankar K. Pal and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.

This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource. Contents: Pattern Recognition: Evolution of Methodologies and Data Mining (A Pal & S K Pal); Adaptive Stochastic Algorithms for Pattern Classification (M A L Thathachar & P S Sastry); Shape in Images (K V Mardia); Decision Trees for Classification: A Review and Some New Results (R Kothari & M Dong); Syntactic Pattern Recognition (A K Majumder & A K Ray); Fuzzy Sets as a Logic Canvas for Pattern Recognition (W Pedrycz & N Pizzi); Neural Network Based Pattern Recognition (V David Sanchez A); Networks of Spiking Neurons in Data Mining (K Cios & D M Sala); Genetic Algorithms, Pattern Classification and Neural Networks Design (S Bandyopadhyay et al.); Rough Sets in Pattern Recognition (A Skowron & R Swiniarski); Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods (E H Ruspini & I S Zwir); Writing Speed and Writing Sequence Invariant On-line Handwriting Recognition (S-H Cha & S N Srihari); Tongue Diagnosis Based on Biometric Pattern Recognition Technology (K Wang et al.); and other papers. Readership: Graduate students, researchers and academics in pattern recognition.

Hybrid Intelligence For Image Analysis And Understanding

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Author : Siddhartha Bhattacharyya
language : en
Publisher: John Wiley & Sons
Release Date : 2017-07-27

Hybrid Intelligence For Image Analysis And Understanding written by Siddhartha Bhattacharyya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-27 with Technology & Engineering categories.

A synergy of techniques on hybrid intelligence for real-life image analysis Hybrid Intelligence for Image Analysis and Understanding brings together research on the latest results and progress in the development of hybrid intelligent techniques for faithful image analysis and understanding. As such, the focus is on the methods of computational intelligence, with an emphasis on hybrid intelligent methods applied to image analysis and understanding. The book offers a diverse range of hybrid intelligence techniques under the umbrellas of image thresholding, image segmentation, image analysis and video analysis. Key features: Provides in-depth analysis of hybrid intelligent paradigms. Divided into self-contained chapters. Provides ample case studies, illustrations and photographs of real-life examples to illustrate findings and applications of different hybrid intelligent paradigms. Offers new solutions to recent problems in computer science, specifically in the application of hybrid intelligent techniques for image analysis and understanding, using well-known contemporary algorithms. The book is essential reading for lecturers, researchers and graduate students in electrical engineering and computer science.