pattern recognition fuzzy objective de bezdek james (16 résultats)

Etat : Good. Ships from the UK. Used book that is in clean, average condition without any missing pages.

Hardcover. Etat : Very Good. No Jacket. Former library book; Missing dust jacket; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.2.

Etat : Used - Very Good. 1981. Hardcover. Cloth, dj. Some shelf-wear. Small tear at head of spine. Else clean copy. Very Good.

No jacket. Etat : Bon. Ancien livre de bibliothèque. Traces d'usure sur la couverture. Salissures sur la tranche. Sans jaquette. Couverture différente. Edition 1981. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Stains on the edge. No dust jacket. Different cover. Edition 1981. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Etat : New.

Hardcover. Etat : Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority!

Etat : New. In.

Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.

Taschenbuch. Etat : 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 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.

Hardcover. Etat : 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. Etat : 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 gold lettering along spine; 272 pages; "Pattern Recognition with Fuzzy Objective Function Algorithms (Advanced Applications in Pattern Recognition)," by James C. Bezdek.

Paperback. Etat : Brand New. reprint edition. 272 pages. 8.98x5.98x0.71 inches. In Stock.

Paperback. Etat : Like New. Like New. book.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 o.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 276 pp. Englisch.

Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 438.