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» Learning and Generalization with the Information Bottleneck
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NIPS
2004
14 years 11 months ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen
NAACL
2010
14 years 7 months ago
Unsupervised Model Adaptation using Information-Theoretic Criterion
In this paper we propose a novel general framework for unsupervised model adaptation. Our method is based on entropy which has been used previously as a regularizer in semi-superv...
Ariya Rastrow, Frederick Jelinek, Abhinav Sethy, B...
CIKM
2009
Springer
15 years 4 months ago
A machine learning approach for improved BM25 retrieval
Despite the widespread use of BM25, there have been few studies examining its effectiveness on a document description over single and multiple field combinations. We determine t...
Krysta Marie Svore, Christopher J. C. Burges
KDD
2010
ACM
257views Data Mining» more  KDD 2010»
15 years 1 months ago
Multi-task learning for boosting with application to web search ranking
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing...
Olivier Chapelle, Pannagadatta K. Shivaswamy, Srin...
SIGIR
2002
ACM
14 years 9 months ago
Bayesian online classifiers for text classification and filtering
This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classifi...
Kian Ming Adam Chai, Hai Leong Chieu, Hwee Tou Ng