image pattern recognition di kovalevsky v a (16 risultati)

Hard Cover. Condizione: Good+. No Jacket. First Edition. Springer-Verlag, 1980, First Edition, 8vo, 241 pages. Hardcover edition, no dust jacket, Ex-Libris, with usual markings, tape pull bottom of front board, else book in good+ condition. Size: 8vo - over 7¾" - 9¾" tall.

gebundene Ausgabe. Condizione: Gut. 238 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten wissenschaftlichen Bibliothek und trägt die entsprechenden Kennzeichnungen (Rückenschild, Instituts-Stempel.); leichte altersbedingte Anbräunung des Papiers; der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. Sprache: Englisch Gewicht in Gramm: 530.

ISBN 0-387-90440-9. Hardback. No Dustjacket. First Printing. Light edgewear, previous owner's name on front free endpaper, otherwise a Tight sound unmarked copy in Very Good condition.

Pp. Condizione: Gut. XI, 241 S. : Ill., graph. Darst. , 25 cm Sofort verfügbar Versand spätestens am nächsten Arbeitstag Rechnung mit ausgewiesener MwSt. liegt bei daily shipping worldwide with invoice ex library in good condition aus Bibliothek in guten Zustand Metody optimal'nych resenij v raspoznavanii izobrazenij , Literaturverz. S. 228 - 234. Sprache: Englisch Gewicht in Gramm: 550.

Format groß 8°, kartoniert, Ecken leicht berieben, 241 Seiten, kleines Fleckchen auf dme Vorderschnitt, dies ist ein regulär ausgesondertes Bibliotheksexemplar aus einer wissenschaftlichen Bibliothek, keine Markierungen / Anmerkungen, das Buch befindet sich in einem guten Zustand --- Hardcover, 241 pages, corners slightlyrubbed, little dot on front cut, Lib.Ex., no marks, the book is in a good condition. Shipping to abroad insured with tracking number.

Condizione: New. In.

Paperback. Condizione: New.

Condizione: New. pp. 256.

Taschenbuch. Condizione: Neu. Image Pattern Recognition | V. A. Kovalevsky | Taschenbuch | 241 S. | Englisch | 2011 | Springer | EAN 9781461260356 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - During the last twenty years the problem of pattern recognition (specifically, image recognition) has been studied intensively by many investigators, yet it is far from being solved. The number of publications increases yearly, but all the experimental results-with the possible exception of some dealing with recognition of printed characters-report a probability of error significantly higher than that reported for the same images by humans. It is widely agreed that ideally the recognition problem could be thought of as a problem in testing statistical hypotheses. However, in most applications the immediate use of even the simplest statistical device runs head on into grave computational difficulties, which cannot be eliminated by recourse to general theory. We must accept the fact that it is impossible to build a universal machine which can learn an arbitrary classification of multidimensional signals. Therefore the solution of the recognition problem must be based on a priori postulates (concerning the sets of signals to be recognized) that will narrow the set of possible classifications, i.e., the set of decision functions. This notion can be taken as the methodological basis for the approach adopted in this book.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -During the last twenty years the problem of pattern recognition (specifically, image recognition) has been studied intensively by many investigators, yet it is far from being solved. The number of publications increases yearly, but all the experimental results-with the possible exception of some dealing with recognition of printed characters-report a probability of error significantly higher than that reported for the same images by humans. It is widely agreed that ideally the recognition problem could be thought of as a problem in testing statistical hypotheses. However, in most applications the immediate use of even the simplest statistical device runs head on into grave computational difficulties, which cannot be eliminated by recourse to general theory. We must accept the fact that it is impossible to build a universal machine which can learn an arbitrary classification of multidimensional signals. Therefore the solution of the recognition problem must be based on a priori postulates (concerning the sets of signals to be recognized) that will narrow the set of possible classifications, i.e., the set of decision functions. This notion can be taken as the methodological basis for the approach adopted in this book. 256 pp. Englisch.

Condizione: New. Print on Demand pp. 256 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 256.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. During the last twenty years the problem of pattern recognition (specifically, image recognition) has been studied intensively by many investigators, yet it is far from being solved. The number of publications increases yearly, but all the experimental resu.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -During the last twenty years the problem of pattern recognition (specifically, image recognition) has been studied intensively by many investigators, yet it is far from being solved. The number of publications increases yearly, but all the experimental results-with the possible exception of some dealing with recognition of printed characters-report a probability of error significantly higher than that reported for the same images by humans. It is widely agreed that ideally the recognition problem could be thought of as a problem in testing statistical hypotheses. However, in most applications the immediate use of even the simplest statistical device runs head on into grave computational difficulties, which cannot be eliminated by recourse to general theory. We must accept the fact that it is impossible to build a universal machine which can learn an arbitrary classification of multidimensional signals. Therefore the solution of the recognition problem must be based on a priori postulates (concerning the sets of signals to be recognized) that will narrow the set of possible classifications, i.e., the set of decision functions. This notion can be taken as the methodological basis for the approach adopted in this book.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 256 pp. Englisch.