Pattern recognition fuzzy objective von bezdek james (9 Ergebnisse)
- Hardcover
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Zustand: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
- Hardcover
Anbieter: Better World Books Ltd, Dunfermline, Vereinigtes KönigreichBetter World Books Ltd
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Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
- Hardcover
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Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Dust jacket in good condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:0306406713.
- Hardcover
- Erstausgabe
- Signiert
Anbieter: Powell's Bookstores Chicago, ABAA, Chicago, IL, USAPowell's Bookstores Chicago, ABAA
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hardcover. Zustand: Used-Very Good. 1st Edition. Cloth, dj. Some shelf-wear. Small tear at head of spine. Else clean copy.
- Hardcover
Anbieter: Salish Sea Books, Bellingham, WA, USASalish Sea Books
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EUR 119,08
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Hardcover. Zustand: Very Good. 0306406713 Very Good+ in a Good+ Jacket; Hardcover; 1981, Springer-Verlag Publishing; Jacket is lightly edgeworn with one medium edge-tear, but is otherwise clean and intact (Now fitted with a new, Brodart jacket protector); Light wear to boards with "straight" edge-corners; Pages clean & unmarked;… Tight binding with straight spine; Blue dust jacket with title in white lettering; 272 pages; "Pattern Recognition with Fuzzy Objective Function Algorithms (Advanced Applications in Pattern Recognition)," by James C. Bezdek.
- Hardcover
Anbieter: Salish Sea Books, Bellingham, WA, USASalish Sea Books
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Hardcover. Zustand: Good. 0306406713 Good; Hardcover, No Jacket; 1981, Springer-Verlag Publishing; Former library copy with standard library markings; Moderate wear to covers with sun-fading to spine; Library stamps to endpapers; Text pages clean & unmarked; Good binding with straight spine; Purple cloth covers with title in gol…d lettering along spine; 272 pages; "Pattern Recognition with Fuzzy Objective Function Algorithms (Advanced Applications in Pattern Recognition)," by James C. Bezdek.
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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EUR 128,53
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Zustand: New. In.
- Softcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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EUR 164,29
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Paperback. Zustand: Brand New. reprint edition. 272 pages. 8.98x5.98x0.71 inches. In Stock.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 que…stion 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.
