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JMLR
2008
230views more  JMLR 2008»
13 years 7 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
JMLR
2008
168views more  JMLR 2008»
13 years 7 months ago
Max-margin Classification of Data with Absent Features
We consider the problem of learning classifiers in structured domains, where some objects have a subset of features that are inherently absent due to complex relationships between...
Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbe...
JACM
2006
99views more  JACM 2006»
13 years 7 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
JAIR
2006
134views more  JAIR 2006»
13 years 7 months ago
Multi-Issue Negotiation with Deadlines
This paper studies bilateral multi-issue negotiation between self-interested autonomous agents. Now, there are a number of different procedures that can be used for this process; ...
S. Shaheen Fatima, Michael Wooldridge, Nicholas R....
JCB
2006
107views more  JCB 2006»
13 years 7 months ago
Maximum Likelihood Molecular Clock Comb: Analytic Solutions
Maximum likelihood (ML) is increasingly used as an optimality criterion for selecting evolutionary trees, but finding the global optimum is a hard computational task. Because no g...
Benny Chor, Amit Khetan, Sagi Snir
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