Pattern recognition in soft computing paradigm /

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Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features us...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Pal, Nikhil R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; New Jersey : World Scientific, ©2001.
Colección:FLSI soft computing series ; v. 2.
Temas:
Pattern recognition systems.
Reconnaissance des formes (Informatique)
COMPUTERS > Optical Data Processing.
Conference papers and proceedings.
Acceso en línea:Texto completo

MARC

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245 0 0 |a Pattern recognition in soft computing paradigm /  |c editor Nikhil R. Pal. 
260 |a Singapore ;  |a New Jersey :  |b World Scientific,  |c ©2001. 
300 |a 1 online resource (xvi, 393 pages) :  |b illustrations, portraits 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a FLSI soft computing series ;  |v v. 2 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
520 |a Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features used to realise the system. A distinguishing feature of this volume is that it deals with all three aspects of PR, namely feature analysis, clustering and classifier design. It also encompasses image processing methodologies and image retrieval with subjective information. The other interesting aspect. 
505 0 |a Series Editor's Preface ; Volume Editor's Preface ; Chapter 1 Dimensionality Reduction Techniques for Interactive Visualization, Exploratory Data Analysis and Classification; 1.1 Introduction ; 1.2 Feature Extraction and Multivariate Data Projection ; 1.3 Interactive Data Visualisation and Explorative Analysis 
505 8 |a 1.4 Advanced Projection Methods 1.5 Conclusions and Future Work ; References ; Chapter 2 The Self-Organizing Map as a Tool in Knowledge Engineering ; 2.1 Introduction ; 2.2 Data analysis using the Self-Organizing Map ; 2.3 Visualization ; 2.4 Software ; 2.5 Case studies 
505 8 |a 2.6 Conclusions 2.7 Acknowledgments ; References ; Chapter 3 Classification of Oceanic Water Types Using Self-organizing Feature Maps ; 3.1 Introduction ; 3.2 Unsupervised neural networks for ocean colour data processing 
505 8 |a 3.3 Hierarchy of neural networks for the water type classification 3.4 Accomplishments of the hierarchical image processing ; 3.5 Conclusions ; References ; Chapter 4 Feature Selection by Artificial Neural Network for Pattern Classification ; 4.1 Introduction 
505 8 |a 4.2 Fractal Neural Network Model 4.3 Feature Selection Algorithm ; 4.4 Simulation and Results ; 4.5 Discussion and Conclusion ; References ; Chapter 5 MLP Based Character Recognition using Fuzzy Features and a Genetic Algorithm for Feature Selection ; 5.1 Introduction 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Pattern recognition systems. 
650 6 |a Reconnaissance des formes (Informatique) 
650 7 |a COMPUTERS  |x Optical Data Processing.  |2 bisacsh 
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655 7 |a Conference papers and proceedings.  |2 fast  |0 (OCoLC)fst01423772 
700 1 |a Pal, Nikhil R. 
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830 0 |a FLSI soft computing series ;  |v v. 2. 
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