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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 570)
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Table of contents (11 chapters)
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Front Matter
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Knowledge Discovery
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Front Matter
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Knowledge Representation
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Front Matter
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Machine Learning
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Front Matter
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Cartesian Granule Features
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Front Matter
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Applications
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Front Matter
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Back Matter
About this book
Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. naïve Bayes and Bayesian networks, Dempster-Shafer theory, mass assignment theory, and others). In addition to describing many state-of-the-art soft computing approaches to knowledge discovery, the author introduces Cartesian granule features and their corresponding learning algorithms as an intuitive approach to knowledge discovery. This new approach embraces the synergistic spirit of soft computing and exploits uncertainty in order to achieve tractability, transparency and generalization. Parallels are drawn between this approach and other well known approaches (such as naive Bayes and decision trees) leading to equivalences under certain conditions.
The approaches presented are further illustrated in a battery of both artificial and real-world problems. Knowledge discovery in real-world problems, such as object recognition in outdoor scenes, medical diagnosis and control, is described in detail. These case studies provide further examples of how to apply the presented concepts and algorithms to practical problems.
The author provides web page access to an online bibliography, datasets, source codes for several algorithms described in the book, and other information.
Soft Computing for Knowledge Discovery is for advanced undergraduates,professionals and researchers in computer science, engineering and business information systems who work or have an interest in the dynamic fields of knowledge discovery and soft computing.
Authors and Affiliations
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Xerox Research Centre Europe (XRCE), Grenoble Laboratory, Meylan, France
James G. Shanahan
Bibliographic Information
Book Title: Soft Computing for Knowledge Discovery
Book Subtitle: Introducing Cartesian Granule Features
Authors: James G. Shanahan
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-4335-0
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2000
Hardcover ISBN: 978-0-7923-7918-8Published: 31 August 2000
Softcover ISBN: 978-1-4613-6947-9Published: 09 November 2012
eBook ISBN: 978-1-4615-4335-0Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XXI, 326
Topics: Information Systems Applications (incl. Internet), Mathematical Logic and Foundations, Artificial Intelligence, Data Structures and Information Theory, Computer Science, general