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» A New Autocalibration Algorithm: Experimental Evaluation
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CIKM
2011
Springer
12 years 7 months ago
Toward interactive training and evaluation
Machine learning often relies on costly labeled data, and this impedes its application to new classification and information extraction problems. This has motivated the developme...
Gregory Druck, Andrew McCallum
VLDB
2004
ACM
119views Database» more  VLDB 2004»
14 years 7 months ago
Evaluating holistic aggregators efficiently for very large datasets
Indatawarehousingapplications,numerousOLAP queries involve the processing of holistic aggregators such as computing the "top n," median, quantiles, etc. In this paper, we...
Lixin Fu, Sanguthevar Rajasekaran
ICDM
2003
IEEE
181views Data Mining» more  ICDM 2003»
14 years 23 days ago
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
Jeremy Z. Kolter, Marcus A. Maloof
PKDD
2004
Springer
102views Data Mining» more  PKDD 2004»
14 years 25 days ago
Improving the Performance of the RISE Algorithm
Ideally, a multi-strategy learning algorithm performs better than its component approaches. RISE is a multi-strategy algorithm that combines rule induction and instance-based learn...
Aloísio Carlos de Pina, Gerson Zaverucha
ICS
1994
Tsinghua U.
13 years 11 months ago
Evaluating automatic parallelization for efficient execution on shared-memory multiprocessors
We present a parallel code generation algorithm for complete applications and a new experimental methodology that tests the efficacy of our approach. The algorithm optimizes for d...
Kathryn S. McKinley