Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery and Soft Granular Computing (Chapman & Hall/CRC Computer Science & Data Analysis)

Computers & Technology

Pal, Sankar K.

Mitra, Pabitra

Computer Science

Chapman and Hall/CRC

This book aims to present results of investigations, both experimental and theoretical, into the effectiveness of fuzzy algorithms as classification tools in some problems concerned with the field of pattern recognition and image processing. Compares results to those obtained with statistical classification techniques.

Fuzzy Mathematical Approach to Pattern Recognition

Science & Math

Dutta Majumder, Dwijesh K.

Mathematics

wiley

Provides a self-contained description of this important aspect of information processing and decision support technology. Presents basic definitions, principles, applications, and a detailed bibliography. Covers a range of real-world examples including control, data mining, and pattern recognition.

Foundations of Soft Case-Based Reasoning

Engineering & Transportation

Shiu, Simon C. K.

Engineering

Wiley-Interscience

The neuro-fuzzy approach to pattern recognition-a unique overview Recent years have seen a surge of interest in neuro-fuzzy computing, which combines fuzzy logic, neural networks, and soft computing techniques. This book focuses on the application of this new tool to the rapidly evolving area of pattern recognition. Written by two leaders in neural networks and soft computing research, this landmark work presents a unified, comprehensive treatment of the state of the art in the field. The authors consolidate a wealth of information previously cattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network-classification, feature evaluation, rule generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems. Clear, concise, and fully referenced, Neuro-Fuzzy Pattern Recognition features extensive examples and highlights key applications in speech, machine learning, medicine, and forensic science. It is an extremely useful resource for scientists and engineers in laboratories and industry as well as for anyone seeking up-to-date information on the advantages of neuro-fuzzy pattern recognition in new computer technologies.

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Mitra, Sushmita

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing 
Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. 
Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: 
<ul>
<li>Soft computing in pattern recognition and data mining</li> <li>A mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set</li> <li>Selection of non-redundant and relevant features of real-valued data sets</li> <li>Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis</li> <li>Segmentation of brain MR images for visualization of human tissues</li> </ul>
Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text―covering the latest findings as well as directions for future research―is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

Maji, Pradipta

Wiley-IEEE Computer Society Pr

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.

Genetic Algorithms for Pattern Recognition (CRC Press Revivals)

Wang, Paul P.

CRC-Press

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.

Granular Neural Networks, Pattern Recognition and Bioinformatics (Studies in Computational Intelligence, 712)

Ray, Shubhra S.

Ganivada, Avatharam

Springer

Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&amp;D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.

Neural Networks and Systolic Array Design

Certification

World Scientific Pub Co Inc

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