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» Least Expected Cost Query Optimization: What Can We Expect
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ECIR
2010
Springer
13 years 6 months ago
Goal-Driven Collaborative Filtering - A Directional Error Based Approach
Collaborative filtering is one of the most effective techniques for making personalized content recommendation. In the literature, a common experimental setup in the modeling phase...
Tamas Jambor, Jun Wang
SIGMOD
2010
ACM
221views Database» more  SIGMOD 2010»
13 years 5 months ago
Analyzing the energy efficiency of a database server
Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimat...
Dimitris Tsirogiannis, Stavros Harizopoulos, Mehul...
GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
13 years 6 months ago
Informative sampling for large unbalanced data sets
Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is ...
Zhenyu Lu, Anand I. Rughani, Bruce I. Tranmer, Jos...
UAI
2004
13 years 6 months ago
Active Model Selection
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
Omid Madani, Daniel J. Lizotte, Russell Greiner
GECCO
2004
Springer
110views Optimization» more  GECCO 2004»
13 years 10 months ago
Is the Predicted ESS in the Sequential Assessment Game Evolvable?
The Sequential Assessment Game model of animal contests predicts an evolutionarily stable strategy (ESS) that is a sequence of thresholds for giving up. Simulated evolution experim...
Winfried Just, Xiaolu Sun