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» Boosting Methods for Regression
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110
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ICML
2008
IEEE
16 years 3 months ago
An empirical evaluation of supervised learning in high dimensions
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
Rich Caruana, Nikolaos Karampatziakis, Ainur Yesse...
73
Voted
LREC
2010
150views Education» more  LREC 2010»
15 years 4 months ago
Achieving Domain Specificity in SMT without Overt Siloing
We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entit...
William D. Lewis, Chris Wendt, David Bullock
130
Voted
VMV
2001
101views Visualization» more  VMV 2001»
15 years 4 months ago
Multiresolution Implicit Object Modeling
In this paper we discuss two image-based 3D modeling methods based on a multi-resolution evolution of a volumetric function's levelset. In the former the role of the levelset...
Augusto Sarti, Stefano Tubaro
116
Voted
ML
2000
ACM
15 years 2 months ago
Randomizing Outputs to Increase Prediction Accuracy
Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, grow predictors on these perturbed training sets and combine them. A que...
Leo Breiman
ACL
2009
15 years 14 days ago
Bilingual Co-Training for Monolingual Hyponymy-Relation Acquisition
This paper proposes a novel framework called bilingual co-training for a largescale, accurate acquisition method for monolingual semantic knowledge. In this framework, we combine ...
Jong-Hoon Oh, Kiyotaka Uchimoto, Kentaro Torisawa