Pattern Recognition and Machine Learning Christopher M. Bishop

By:

Bishop, Christopher M

Material type: Text

TextLanguage: English Publication details: Springer 2006 New YorkEdition: 1st EdDescription: 738p. 17.78 x 3.84 x 25.4 cmISBN:

9781493938438

Subject(s):

DDC classification:

006.42 BIS

Summary: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Call number	Status	Barcode
Books	Rashtriya Raksha University	006.4 BIS (Browse shelf(Opens below))	Available	11508
Books	Rashtriya Raksha University	6.42 BIS (Browse shelf(Opens below))	Available	8122
Books	Rashtriya Raksha University	6.42 BIS (Browse shelf(Opens below))	Available	8123

Browsing Rashtriya Raksha University shelves Close shelf browser (Hides shelf browser)

Previous	No cover image available No cover image available No cover image available							Next
Previous	006.4 Handbook of biometrics	006.4 Face detection and recognition : theory and practice /	006.4 BIS Pattern Recognition and Machine Learning	006.4 BIS Pattern Recognition and Machine Learning	006.4 MAL Handbook of Fingerprint Recognition	006.4 VEE The Biometric Computing: Recognition and Registration (Hardbound - 2019)	006.4 ZHA Biometric Image Discrimination Technologies : Computational Intelligence And Its Applications	Next

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.

There are no comments on this title.

to post a comment.