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» Practical Preference Relations for Large Data Sets
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JMLR
2006
113views more  JMLR 2006»
15 years 6 months ago
Learning the Structure of Linear Latent Variable Models
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
UAI
2004
15 years 7 months ago
A Hierarchical Graphical Model for Record Linkage
The task of matching co-referent records is known among other names as record linkage. For large record-linkage problems, often there is little or no labeled data available, but u...
Pradeep D. Ravikumar, William W. Cohen
EMNLP
2009
15 years 3 months ago
Mining Search Engine Clickthrough Log for Matching N-gram Features
User clicks on a URL in response to a query are extremely useful predictors of the URL's relevance to that query. Exact match click features tend to suffer from severe data s...
Huihsin Tseng, Longbin Chen, Fan Li, Ziming Zhuang...
TIT
1998
216views more  TIT 1998»
15 years 5 months ago
Data Compression and Harmonic Analysis
In this article we review some recent interactions between harmonic analysis and data compression. The story goes back of course to Shannon’s R(D) theory in the case of Gaussian...
David L. Donoho, Martin Vetterli, Ronald A. DeVore...
SDM
2004
SIAM
165views Data Mining» more  SDM 2004»
15 years 7 months ago
Visualizing RFM Segmentation
Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and imp...
Ron Kohavi, Rajesh Parekh