Pattern recognition in soft computing paradigm / / editor Nikhil R. Pal

• Record Nr.: UNINA9911135326303321
• Pubbl/distr/stampa: Singapore ; ; New Jersey, : World Scientific, c2001
• ISBN: 9786611960674; 9781281960672; 1281960675; 9789812811691; 9812811699
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (411 p.)
• Collana: FLSI soft computing series ; ; 2
• Altri autori (Persone): PalNikhil R
• Disciplina: 006.4
• Soggetti: Pattern recognition systems; Pattern perception
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Contents; 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; 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; 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; 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; 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; 5.2 Hough Transform and Multilayer Perceptron
• Sommario/riassunto: 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