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EMNLP
2009
13 years 6 months ago
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The res...
Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie...
ICADL
2010
Springer
160views Education» more  ICADL 2010»
14 years 1 months ago
Thesaurus Extension Using Web Search Engines
Maintaining and extending large thesauri is an important challenge facing digital libraries and IT businesses alike. In this paper we describe a method building on and extending ex...
Robert Meusel, Mathias Niepert, Kai Eckert, Heiner...
CVPR
2001
IEEE
14 years 10 months ago
Clustering Art
We extend a recently developed method [1] for learning the semantics of image databases using text and pictures. We incorporate statistical natural language processing in order to...
Kobus Barnard, Pinar Duygulu, David A. Forsyth
ML
1998
ACM
139views Machine Learning» more  ML 1998»
13 years 8 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby
HICSS
2006
IEEE
163views Biometrics» more  HICSS 2006»
14 years 2 months ago
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or gene...
Dmitri Roussinov, Weiguo Fan