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» Learning consumer preferences using semantic similarity
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UAI
2003
13 years 11 months ago
Learning Module Networks
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
ICDE
2006
IEEE
166views Database» more  ICDE 2006»
14 years 11 months ago
WebIQ: Learning from the Web to Match Deep-Web Query Interfaces
Integrating Deep Web sources requires highly accurate semantic matches between the attributes of the source query interfaces. These matches are usually established by comparing th...
Wensheng Wu, AnHai Doan, Clement T. Yu
NIPS
1997
13 years 11 months ago
Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report
Singular value decomposition (SVD) can be viewed as a method for unsupervised training of a network that associates two classes of events reciprocally by linear connections throug...
Thomas K. Landauer, Darrell Laham, Peter W. Foltz
AIM
2005
13 years 9 months ago
Semantic Integration in Text: From Ambiguous Names to Identifiable Entities
Intelligent access to information requires semantic integration of structured databases with unstructured textual resources. While the semantic integration problem has been widely...
Xin Li, Paul Morie, Dan Roth
SDM
2011
SIAM
370views Data Mining» more  SDM 2011»
13 years 14 days ago
Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....