Probabilistic Graphical Models Principles and Applications /
This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applicat...
Main Author: | Sucar, Luis Enrique (Author) |
---|---|
Corporate Author: | SpringerLink (Online service) |
Format: | Electronic eBook |
Language: | English |
Published: |
London :
Springer London : Imprint: Springer,
2015.
|
Series: | Advances in Computer Vision and Pattern Recognition,
|
Subjects: | |
Online Access: | Full Text via HEAL-Link |
Similar Items
-
Principles and Theory for Data Mining and Machine Learning
by: Clarke, Bertrand, et al.
Published: (2009) -
Information Theory in Computer Vision and Pattern Recognition
by: Escolano, Francisco, et al.
Published: (2009) -
Hebbian Learning and Negative Feedback Networks
by: Fyfe, Colin
Published: (2005) -
Learning to Rank for Information Retrieval
by: Liu, Tie-Yan
Published: (2011) -
Introduction to Data Science A Python Approach to Concepts, Techniques and Applications /
by: Igual, Laura, et al.
Published: (2017)