Approximate inference, structure learning and feature estimation in Markov random fields

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Approximate inference, structure learning and feature estimation in Markov random fields: thesis abstract

Full text

Pdf (106 KB)

Source

ACM SIGKDD Explorations Newsletter archive
Volume 10 , Issue 2 (December 2008) table of contents

COLUMN: PhD dissertation abstracts table of contents

Pages 32-33

Year of Publication: 2008

ISSN:1931-0145

Author

Pradeep Ravikumar

University of California, Berkeley

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 18, Citation Count: 0

Additional Information:

references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1540276.1540286 What is a DOI?

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	S.L. Lauritzen. Graphical Models. Oxford University Press, 1996.
	2	P. Ravikumar. Approximate inference, structure learning and feature estimation in markov random fields. Technical Report CMU-ML-07-115, Carnegie Mellon University, 2007.