Isbn: 9780387946184 - a probabilistic theory of pattern recognition: 31 (stochastic modelling and applied probability, 31) (22 resultados)

Hardcover. Condición: Good. No Jacket. used hardcover copy, lacking a dust jacket (as issued). light shelfwear, corners perhaps slightly bumped. some visible creases on spine, bt binding is intact. some scuffing to bottom edge of page block. pages and binding are clean, straight, and tight. there are no marks to the text or other serious flaws.…

Condición: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail.…

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Condición: 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.

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Hardcover. Condición: Gut. Corr. 2. printing. XV, 636 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. M16116 9780387946184 Sprache: Englisch Gewicht in Gramm: 1100.

Condición: New. pp. 660.

Condición: New. pp. 660 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condición: New. pp. 660.

Condición: New.

Condición: New.

hardcover. Condición: New. In shrink wrap. Looks like an interesting title.

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condición: As New. Unread book in perfect condition.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material. 660 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and ex.…

Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Pattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 660 pp. Englisch.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material.…