Combining pattern classifiers methods par kuncheva ludmila (22 résultats)

Etat : good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.

Etat : 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.

Etat : Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

Hardcover. Etat : Good. A unified, coherent, and expansive treatment of current classifier ensemble methodsMail sorting, medical test reading, military target recognition, signature verification, meteorological forecast, DNA matching, fingerprint recognition. These are just a few of the areas requiring reliable, precise pattern recognition. Although in the past, pattern recognition has focused on designing single classifiers, recently the focus has been on combining several classifiers and getting a consensus of results for greater accuracy. This interest in combining classifiers has grown astronomically in recent years, evolving into a rich and dynamic, if loosely structured, discipline. Combining Pattern Classifiers: Methods and Algorithms represents the first attempt to provide a comprehensive survey of this fastgrowing field. In a clear and straightforward manner, the author provides a muchneeded road map through a multifaceted and often controversial subject while effectively organizing and systematizing the current state of the art. Covering a broad range of methodologies, algorithms, and theories, the text addresses such questions as: Why should we combine classifiers? What are the current approaches for building classifier ensembles? What fusion methods can we use? How do we measure diversity in a classifier ensemble and is diversity really a key factor to its success? Replete with case studies and realworld applications, this groundbreaking text will be of interest to academics and researchers in the field seeking both new classification tools and new uses for the old ones. Copyright © John Wiley.…

Etat : Like New. hardcover. Text block firm and clean, binding unblemished, boards straight, without highlights or underlining. Fine, like new condition. Supporting Bay Area Friends of the Library since 2010. Well packaged and promptly shipped.

Hardcover. Etat : Fine. 1st Edition. No Markings.

Etat : New.

Etat : new.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : As New. Unread book in perfect condition.

Etat : New. pp. 384.

Etat : New. In English.

Etat : 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. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780471210788. …

Hardcover. Etat : New.

Etat : New. pp. 384.

Hardcover. Etat : Brand New. 2nd edition. 350 pages. 9.00x6.00x1.00 inches. In Stock.

Etat : Sehr gut. Zustand: Sehr gut | Seiten: 376 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Hardcover. Etat : Brand New. 2nd edition. 350 pages. 9.00x6.00x1.00 inches. In Stock. This item is printed on demand.

Etat : New. PRINT ON DEMAND pp. 384.