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Books > Computing & IT > Computer hardware & operating systems > Systems management
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Soft Computing for Knowledge Discovery - Introducing Cartesian Granule Features (Hardcover, 2000 ed.)
Loot Price: R4,473
Discovery Miles 44 730
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Soft Computing for Knowledge Discovery - Introducing Cartesian Granule Features (Hardcover, 2000 ed.)
Series: The Springer International Series in Engineering and Computer Science, 570
Expected to ship within 12 - 17 working days
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Knowledge discovery is an area of computer science that attempts to
uncover interesting and useful patterns in data that permit a
computer to perform a task autonomously or assist a human in
performing a task more efficiently. 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. naive 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.
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