Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science)

Shanahan, James G.

ISBN 10: 0792379187 / ISBN 13: 9780792379188

Editore: Springer, 2000

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326 pp., Hardcover, minor marginalia to 1 page else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Codice articolo ZB1097015

Dati bibliografici

• Titolo: Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science)
• Editore: Springer
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 0792379187
• ISBN 13: 9780792379188
• Rilegatura: Rilegato
• Condizione: Fine

Descrizione articolo

Riassunto

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. 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.

Contenuti

Note to the Reader. Foreword. Preface. Part I: 1. Knowledge Discovery. Part II: 2. Knowledge Representation. 3. Fuzzy Set Theory. 4. Fuzzy Logic. 5. Probability Theory. 6. Fril - A Support Logic Programming Environment. Part III: 7. Machine Learning. Part IV: 8. Cartesian Granule Features. 9. Learning Cartesian Granule Feature Models. Part V: 10. Analysis of Cartesian Granule Feature Models. 11. Applications. Appendix: Evolutionary Computation. Glossary of Main Symbols. Subject Index.