Mustererkennung und maschinelles Lernen [Informationswissenschaft und Statistik]

Motif Cafe
(51)
Angemeldet als gewerblicher Verkäufer
Mehr Artikel des Verkäufers
Verkäufer kontaktieren
US $61,79
Ca.CHF 49,00
Artikelzustand:
Neu
Ganz entspannt. Rückgaben akzeptiert.
Versand:
Kostenlos USPS Media MailTM.
Standort: Clifton, New Jersey, USA
Lieferung:
Lieferung zwischen Di, 21. Okt und Mo, 27. Okt nach 94104 bei heutigem Zahlungseingang
Wir wenden ein spezielles Verfahren zur Einschätzung des Liefertermins an – in diese Schätzung fließen Faktoren wie die Entfernung des Käufers zum Artikelstandort, der gewählte Versandservice, die bisher versandten Artikel des Verkäufers und weitere ein. Insbesondere während saisonaler Spitzenzeiten können die Lieferzeiten abweichen.
Rücknahme:
30 Tage Rückgabe. Käufer zahlt Rückversand. Wenn Sie ein eBay-Versandetikett verwenden, werden die Kosten dafür von Ihrer Rückerstattung abgezogen.
Zahlungen:
     Diners Club

Sicher einkaufen

eBay-Käuferschutz
Geld zurück, wenn etwas mit diesem Artikel nicht stimmt. Mehr erfahreneBay-Käuferschutz - wird in neuem Fenster oder Tab geöffnet
Der Verkäufer ist für dieses Angebot verantwortlich.
eBay-Artikelnr.:305728153305
Zuletzt aktualisiert am 25. Mär. 2025 01:11:05 MEZAlle Änderungen ansehenAlle Änderungen ansehen

Artikelmerkmale

Artikelzustand
Neu: Neues, ungelesenes, ungebrauchtes Buch in makellosem Zustand ohne fehlende oder beschädigte ...
ISBN
9780387310732
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Springer New York
ISBN-10
0387310738
ISBN-13
9780387310732
eBay Product ID (ePID)
51822478

Product Key Features

Number of Pages
Xx, 778 Pages
Publication Name
Pattern Recognition and Machine Learning
Language
English
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
Publication Year
2006
Type
Textbook
Subject Area
Mathematics, Computers, Psychology
Author
Christopher M. Bishop
Series
Information Science and Statistics Ser.
Format
Hardcover

Dimensions

Item Height
0.7 in
Item Weight
75.7 Oz
Item Length
9.3 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2006-922522
Reviews
"This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software, From the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ?pattern recognition? or ?machine learning?'. ? This book will serve as an excellent reference. ? With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop?'s book is a useful introduction ? and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007)
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.4
Table Of Content
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
Synopsis
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same ?eld, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had signi'cant impact on both algorithms and applications. This new textbook re'ects these recent developments while providing a comp- hensive introduction to the ?elds of pattern recognition and machine learning. It is aimed at advanced undergraduates or ?rst year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not - sential as the book includes a self-contained introduction to basic probability theory., The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained byinstructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Coming soon: *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) *For instructors, worked solutions to remaining exercises from the Springer web site *Lecture slides to accompany each chapter *Data sets available for download, Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory., This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions., This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.
LC Classification Number
Q337.5

Artikelbeschreibung des Verkäufers

Info zu diesem Verkäufer

Motif Cafe

91,3% positive Bewertungen•267 Artikel verkauft

Mitglied seit Jan 2019
Angemeldet als gewerblicher Verkäufer
Shop besuchenKontakt

Detaillierte Verkäuferbewertungen

Durchschnitt in den letzten 12 Monaten
Genaue Beschreibung
--
Angemessene Versandkosten
5.0
Lieferzeit
4.8
Kommunikation
4.7

Verkäuferbewertungen (57)

Alle Bewertungenselected
Positiv
Neutral
Negativ
  • v***u (254)- Bewertung vom Käufer.
    Letztes Jahr
    Bestätigter Kauf
    There was a issue with this manual, the seller didn't realize it was a 2 volume set. Only received volume 2 of 2. When contacted, seller took responsibility for an inaccurate listing, and offered an agreeable solution to the dilemma. In the end, I am happy with the resolution. Mistakes happen, a good seller takes responsibility and does the right thing. Win/Win for both parties.
    Volkswagen Passat [B5] Service Manual: 1998, 1999, 2000, 2001, 2002, 2003, 2004, (Nr. 305872213162)
  • r***r (409)- Bewertung vom Käufer.
    Letztes Jahr
    Bestätigter Kauf
    I am so happy to have received this book at such a great price!! It arrived brand new exactly as described. The book arrived quickly and was packaged very well . I highly recommend this seller!!
    The Last Thing She Ever Did (Nr. 305844838772)
  • u***t (820)- Bewertung vom Käufer.
    Letzte 6 Monate
    Bestätigter Kauf
    No communication, no shipment. Garbage seller.
    The Best of Larry Niven (Nr. 305857168647)
Alle Bewertungen ansehen

Noch mehr entdecken: