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Sunday, October 11, 2020 | History

7 edition of Hybrid methods in pattern recognition found in the catalog.

Hybrid methods in pattern recognition

  • 315 Want to read
  • 28 Currently reading

Published by World Scientific in River Edge, N.J .
Written in English

    Subjects:
  • Pattern recognition systems.,
  • Neural networks (Computer science)

  • Edition Notes

    Includes bibliographical references.

    Statementeditors, H. Bunke, A. Kandel.
    SeriesSeries in machine perception and artificial intelligence ;, v. 47
    ContributionsBunke, Horst., Kandel, Abraham.
    Classifications
    LC ClassificationsTK7882.P3 H97 2002
    The Physical Object
    Paginationxii, 324 p. :
    Number of Pages324
    ID Numbers
    Open LibraryOL3438833M
    ISBN 109810248326
    LC Control Number2005297883
    OCLC/WorldCa50196647

    the hybrid method saves great amount of training and testing time in large-scale tasks and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the hybrid method performs better than the nearest neighbour method. 1. Introduction In pattern recognition, one deals with either binary. Here we present a hybrid method of generating a hierarchical recognition system based on example learning. The method is 'hybrid' in that it uses both conventional Artificial Intelligence and Artificial Neural Network techniques. The integrated hierarchical recognition system, called IHKB (integrated hierarchical knowledge base), has a tree structure consisting of nodes and leaves.

    Pattern recognition is the automated recognition of patterns and regularities in has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine n recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use.   Pattern recognition, despite its relatively short history, has already found practical application in many areas of human activity. Systems of pattern recognition usually support people in performing tasks related to ensuring security, including access to premises and devices, detection of unusual changes (e.g. in medicine, cartography, geology), diagnosing technical conditions of devices, .

    The method is 'hybrid' in that it uses both conventional Artificial Intelligence and Artificial Neural Network techniques. The integrated hierarchical recognition system, called IHKB (integrated hierarchical knowledge base), has a tree structure consisting of nodes and leaves. Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.


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Hybrid methods in pattern recognition Download PDF EPUB FB2

Hybrid Methods in Pattern Recognition [Bunke, Horst, Kandel, Abraham] on *FREE* shipping on qualifying offers. Hybrid Methods in Pattern Recognition. Hybrid methods aim at combining the advantages of different paradigms within a single Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field.

Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and s.

A collection of articles describing progress in the field of hybrid methods in pattern recognition. It explores the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, and more.

Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and subsymbolic learning, and others.

Bunke H. () Hybrid Methods in Pattern Recognition. In: Devijver P.A., Kittler J. (eds) Pattern Recognition Theory and Applications.

NATO ASI Series (Series F: Computer and Systems Sciences), vol Springer, Berlin, Heidelberg. DOI ; Publisher Name Springer, Berlin, Heidelberg; Print ISBN Cited by: Patricia Melin, Oscar Castillo. This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms.

Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. This paper presents a novel hybrid intelligent method (HIM) for recognition of common types of CCP.

The proposed method includes three main modules: a feature extraction module, a classifier module and an optimization module. The first section covers a broad spectrum of pattern recognition methodologies, including geometric, statistical, fuzzy set, syntactic, graph-theoretic and hybrid approaches.

Its cove,r­ age of hybrid methods places the volume in a unique position among existing books on pattern recognition. Pattern recognition is widely used to study the patterns in the data through the applications and implementation of extensive algorithms which are either classification based or clustering based.

Series in Machine Perception and Artificial Intelligence Hybrid Methods in Pattern Recognition, pp. () No Access FUZZIFICATION OF NEURAL. Purchase Syntactic Methods in Pattern Recognition, Volume - 1st Edition. Print Book & E-Book. ISBNmethods of soft computing methods for better accuracy and performance is the need of an hour.

A brief review is presented below. Fuzzy Logic in Pattern Recognition: Dealing with uncertainties is a common problem in pattern recognition and the use of fuzzy set theory to a lot of new methods of pattern recognition.

Fuzzy set theory. This book describes the latest advances in fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems.

hybrid intelligent systems to solve this problem. We develop a hybrid of genetic programming, neural network and multiple regression. In this article, we present new method for statistical pattern recognition.

Statistical pattern recognition relates to the use of statistical techniques for analyzing data measurements in order to extract. ANN-TREE: a hybrid method for pattern recognition ANN-TREE: a hybrid method for pattern recognition Zhou, Lijia ABSTRACT Here we present a hybrid method of generating a hierarchical recognition system based on example learning.

The method is 'hybrid" in that it uses both conventional Artificial Intelligence and Artificial Neural Network techniques. Hybrid Methods:Hybrid face recognition systems use a combination of both holistic and feature extraction methods.

Generally 3D Images are used in hybrid methods. The book provides a comprehensive view of Pattern Recognition concepts and methods, illustrated with real-life applications in several areas.

It is appropriate as a textbook of Pattern Recognition courses and also for professionals and researchers who need to apply Pattern Recognition techniques. These are explained in a unified an innovative way, with multiple examples enhacing the.

through “careful reading and re-reading of the data” (Rice & Ezzy,p. It is a form of pattern recognition within the data, where emerging themes become the categories for analysis. The method of analysis chosen for this study was a hybrid approach of qualitative methods of thematic.

While these methods are now part of our standard toolkit, Isabelle has moved on to design benchmarks for tasks that are harder to evaluate. This is not only a great service to the com-munity, but it will also enable scientific progress on problems that are arguably more difficult than classical pattern recognition.

High accuracy and short amount of time are required for the solutions of many classification problems such as real-world classification problems. Due to the practical importance of many classification problems (such as crime detection), many algorithms have been developed to tackle them.

For years, metaheuristics (MHs) have been successfully used for solving classification problems.This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms.

Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition.This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.

Soft Computing (SC) consists of several intelligent computing paradigms, including.