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» Learning probabilistic models of the Web
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KDD
2009
ACM
190views Data Mining» more  KDD 2009»
16 years 6 months ago
Named entity mining from click-through data using weakly supervised latent dirichlet allocation
This paper addresses Named Entity Mining (NEM), in which we mine knowledge about named entities such as movies, games, and books from a huge amount of data. NEM is potentially use...
Gu Xu, Shuang-Hong Yang, Hang Li
CIKM
2010
Springer
15 years 4 months ago
Exploiting site-level information to improve web search
Ranking Web search results has long evolved beyond simple bag-of-words retrieval models. Modern search engines routinely employ machine learning ranking that relies on exogenous r...
Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josif...
KDD
2002
ACM
171views Data Mining» more  KDD 2002»
16 years 6 months ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
ICCV
2005
IEEE
15 years 12 months ago
Object Categorization by Learned Universal Visual Dictionary
This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). Compact and yet discriminative appearance-based object class mode...
John M. Winn, Antonio Criminisi, Thomas P. Minka
AIPS
2008
15 years 8 months ago
Stochastic Enforced Hill-Climbing
Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called "basin flooding"). ...
Jia-Hong Wu, Rajesh Kalyanam, Robert Givan