Pattern Recognition and Machine Learning

Viser: Pattern Recognition and Machine Learning

Christopher M. Bishop

(2016)

Sprog: Engelsk

Springer New York

848,00 kr.

ikke på lager, Bestil nu og få den leveret
om ca. 15 hverdage

Porto kan blive tilføjet, se takster her

Du kan vælge at få vare sendt eller afhente den i en af vores butikker. Vælger du at få den sendt koster det:

Levering på adressen
1 - 1 vare – kr.	59.5
2 - 5 varer – kr.	59.5
6 - 10 varer – kr.	59.5
11 - 20 varer – kr.	59.5
21 - 49 varer – kr.	79.5
50 - 9999 varer – kr.	119.5

Levering på firma adressen
1 - 1 vare – kr.	39.5
2 - 5 varer – kr.	39.5
6 - 10 varer – kr.	39.5
11 - 20 varer – kr.	39.5
21 - 49 varer – kr.	79.5
50 - 9999 varer – kr.	119.5

Afhent i postbutik eller pakkeboks
1 - 1 vare – kr.	39.5
2 - 5 varer – kr.	39.5
6 - 10 varer – kr.	39.5
11 - 20 varer – kr.	39.5
21 - 49 varer – kr.	59.5
50 - 9999 varer – kr.	99.5

Læs mere om forsendelse her

Detaljer om varen

Paperback
Udgiver: Springer New York (August 2016)
ISBN: 9781493938438

Beskrivelse
Indhold

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. 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.

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.