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» Detecting Attribute Dependencies from Query Feedback
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PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
13 years 6 months ago
Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Zhao Xu, Kristian Kersting, Thorsten Joachims
CVPR
2007
IEEE
13 years 11 months ago
Diverse Active Ranking for Multimedia Search
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
ShyamSundar Rajaram, Charlie K. Dagli, Nemanja Pet...
ECIR
2009
Springer
14 years 4 months ago
A Comparative Study of Utilizing Topic Models for Information Retrieval
We explore the utility of different types of topic models for retrieval purposes. Based on prior work, we describe several ways that topic models can be integrated into the retrie...
Xing Yi, James Allan
SIGMOD
2004
ACM
147views Database» more  SIGMOD 2004»
14 years 7 months ago
Robust Query Processing through Progressive Optimization
Virtually every commercial query optimizer chooses the best plan for a query using a cost model that relies heavily on accurate cardinality estimation. Cardinality estimation erro...
Volker Markl, Vijayshankar Raman, David E. Simmen,...
VLDB
2001
ACM
190views Database» more  VLDB 2001»
14 years 1 days ago
LEO - DB2's LEarning Optimizer
Most modern DBMS optimizers rely upon a cost model to choose the best query execution plan (QEP) for any given query. Cost estimates are heavily dependent upon the optimizer’s e...
Michael Stillger, Guy M. Lohman, Volker Markl, Mok...