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Fuzzy Classifier Design
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Book Synopsis Fuzzy Classifier Design by : Ludmila I. Kuncheva
Download or read book Fuzzy Classifier Design written by Ludmila I. Kuncheva and published by Physica. This book was released on 2012-11-08 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever since have been a center of many discussions, fervently admired and condemned. Both proponents and opponents consider the argu ments pointless because none of them would step back from their territory. And stiH, discussions burst out from a single sparkle like a conference pa per or a message on some fuzzy-mail newsgroup. Here is an excerpt from an e-mail messagepostedin1993tofuzzy-mail@vexpert. dbai. twvien. ac. at. by somebody who signed "Dave". , . . . Why then the "logic" in "fuzzy logic"? I don't think anyone has successfully used fuzzy sets for logical inference, nor do I think anyone wiH. In my admittedly neophyte opinion, "fuzzy logic" is a misnomer, an oxymoron. (1 would be delighted to be proven wrong on that. ) . . . I carne to the fuzzy literature with an open mind (and open wal let), high hopes and keen interest. I am very much disiHusioned with "fuzzy" per se, but I did happen across some extremely interesting things along the way. " Dave, thanks for the nice quote! Enthusiastic on the surface, are not many of us suspicious deep down? In some books and journals the word fuzzy is religiously avoided: fuzzy set theory is viewed as a second-hand cheap trick whose aim is nothing else but to devalue good classical theories and open up the way to lazy ignorants and newcomers.
Book Synopsis Fuzzy Models and Algorithms for Pattern Recognition and Image Processing by : James C. Bezdek
Download or read book Fuzzy Models and Algorithms for Pattern Recognition and Image Processing written by James C. Bezdek and published by Springer Science & Business Media. This book was released on 2006-09-28 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Pattern Recognition with Fuzzy Objective Function Algorithms by : James C. Bezdek
Download or read book Pattern Recognition with Fuzzy Objective Function Algorithms written by James C. Bezdek and published by Springer Science & Business Media. This book was released on 2013-03-13 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author :James C. Bezdek Publisher :Institute of Electrical & Electronics Engineers(IEEE) ISBN 13 : Total Pages :560 pages Book Rating :4.3/5 (97 download)
Book Synopsis Fuzzy Models for Pattern Recognition by : James C. Bezdek
Download or read book Fuzzy Models for Pattern Recognition written by James C. Bezdek and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 1992 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning Classifier Systems by : Pier L. Lanzi
Download or read book Learning Classifier Systems written by Pier L. Lanzi and published by Springer. This book was released on 2003-06-26 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
Book Synopsis Soft Computing for Knowledge Discovery and Data Mining by : Oded Maimon
Download or read book Soft Computing for Knowledge Discovery and Data Mining written by Oded Maimon and published by Springer Science & Business Media. This book was released on 2007-10-25 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.
Book Synopsis NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM by : S. RAJASEKARAN
Download or read book NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM written by S. RAJASEKARAN and published by PHI Learning Pvt. Ltd.. This book was released on 2003-01-01 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as neuro-fuzzy, fuzzy-genetic, and neuro-genetic systems. The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Associative Memories. The book also gives an exhaustive discussion of FL-GA hybridization. Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book with a wealth of information that is clearly presented and illustrated by many examples and applications is designed for use as a text for courses in soft computing at both the senior undergraduate and first-year post-graduate engineering levels. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.
Book Synopsis Fuzzy Sets & their Application to Clustering & Training by : Beatrice Lazzerini
Download or read book Fuzzy Sets & their Application to Clustering & Training written by Beatrice Lazzerini and published by CRC Press. This book was released on 2000-03-24 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge in the last half-century. Its theoretical and technological promise is vast, and we are only beginning to experience its potential. Clustering is the first and most basic application of fuzzy set theory, but forms the basis of many, more sophisticated, intelligent computational models, particularly in pattern recognition, data mining, adaptive and hierarchical clustering, and classifier design. Fuzzy Sets and their Application to Clustering and Training offers a comprehensive introduction to fuzzy set theory, focusing on the concepts and results needed for training and clustering applications. It provides a unified mathematical framework for fuzzy classification and clustering, a methodology for developing training and classification methods, and a general method for obtaining a variety of fuzzy clustering algorithms. The authors - top experts from around the world - combine their talents to lay a solid foundation for applications of this powerful tool, from the basic concepts and mathematics through the study of various algorithms, to validity functionals and hierarchical clustering. The result is Fuzzy Sets and their Application to Clustering and Training - an outstanding initiation into the world of fuzzy learning classifiers and fuzzy clustering.
Book Synopsis Computational Intelligence for Knowledge-Based System Design by : Eyke Hüllermeier
Download or read book Computational Intelligence for Knowledge-Based System Design written by Eyke Hüllermeier and published by Springer Science & Business Media. This book was released on 2010-06-17 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes the refereed proceedings of the 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2010, held in Dortmund, Germany from June 28 - July 2, 2010. The 77 revised full papers were carefully reviewed and selected from 320 submissions and reflect the richness of research in the field of Computational Intelligence and represent developments on topics as: machine learning, data mining, pattern recognition, uncertainty handling, aggregation and fusion of information as well as logic and knowledge processing.
Book Synopsis New Frontiers in Computational Intelligence and Its Applications by : Masoud Mohammadian
Download or read book New Frontiers in Computational Intelligence and Its Applications written by Masoud Mohammadian and published by IOS Press. This book was released on 2000 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence is a broad and active research area that is growing rapidly due to the many successful applications of these new techniques in very diverse problems. Many industries have benefited from adopting this technology. The increased number of patents and diverse range of products developed using computational intelligence methods is evidence of this fact. The goal of this book is to provide highlights of the current research in computational intelligence area. The book consists of research papers in the fields of neural networks, fuzzy logic, evolutionary computing, hybrid evolutionary computing-fuzzy logic systems, hybrid neural networks-evolutionary computing and fuzzy logic systems, image processing and vision, advances in robotics, control and manufacturing, and rough sets.
Book Synopsis Adaptive and Natural Computing Algorithms by : Mikko Kolehmainen
Download or read book Adaptive and Natural Computing Algorithms written by Mikko Kolehmainen and published by Springer Science & Business Media. This book was released on 2009-10-15 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009. The 63 revised full papers presented were carefully reviewed and selected from a total of 112 submissions. The papers are organized in topical sections on neutral networks, evolutionary computation, learning, soft computing, bioinformatics as well as applications.
Download or read book Fuzzy Modelling written by Witold Pedrycz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.
Book Synopsis Simulated Evolution and Learning by : Lam Thu Bui
Download or read book Simulated Evolution and Learning written by Lam Thu Bui and published by Springer. This book was released on 2012-12-02 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 9th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Hanoi, Vietnam, in December 2012. The 50 full papers presented were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on evolutionary algorithms, theoretical developments, swarm intelligence, data mining, learning methodologies, and real-world applications.
Book Synopsis Simulated Evolution and Learning by : Kalyanmoy Deb
Download or read book Simulated Evolution and Learning written by Kalyanmoy Deb and published by Springer. This book was released on 2010-11-22 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: 6%acceptancerateandshortpapersaddanother13.
Book Synopsis Knowledge Mining Using Intelligent Agents by : Satchidananda Dehuri
Download or read book Knowledge Mining Using Intelligent Agents written by Satchidananda Dehuri and published by World Scientific. This book was released on 2010-12-21 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Mining Using Intelligent Agents explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines — data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy-makers are given an insight into emerging technologies and their possible hybridization, which can be used for activities like dredging, capturing, distributions and the utilization of knowledge in their domain of interest (i.e. business, policy-making, etc.).By studying the behavior of swarm intelligence, this book aims to integrate the computational intelligence paradigm and intelligent distributed agents architecture to optimize various engineering problems and efficiently represent knowledge from the large gamut of data.
Book Synopsis Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases by : Oscar Cordon
Download or read book Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases written by Oscar Cordon and published by World Scientific. This book was released on 2001-07-13 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas.Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms.
Book Synopsis Advances in Swarm Intelligence by : Ying Tan
Download or read book Advances in Swarm Intelligence written by Ying Tan and published by Springer. This book was released on 2016-07-07 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 9712 and LNCS 9713 constitutes the refereed proceedings of the 7th International Conference on Swarm Intelligence, ICSI 2016, held in Bali, Indonesia, in June 2016. The 130 revised regular papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in 22 cohesive sections covering major topics of swarm intelligence and related areas such as trend and models of swarm intelligence research; novel swarm-based optimization algorithms; swarming behaviour; some swarm intelligence algorithms and their applications; hybrid search optimization; particle swarm optimization; PSO applications; ant colony optimization; brain storm optimization; fireworks algorithms; multi-objective optimization; large-scale global optimization; biometrics; scheduling and planning; machine learning methods; clustering algorithm; classification; image classification and encryption; data mining; sensor networks and social networks; neural networks; swarm intelligence in management decision making and operations research; robot control; swarm robotics; intelligent energy and communications systems; and intelligent and interactive and tutoring systems.
