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» Using Machine Learning to Focus Iterative Optimization
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ICML
2007
IEEE
16 years 3 months ago
Boosting for transfer learning
Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumpt...
Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu
BMCBI
2006
100views more  BMCBI 2006»
15 years 3 months ago
Using the nucleotide substitution rate matrix to detect horizontal gene transfer
Background: Horizontal gene transfer (HGT) has allowed bacteria to evolve many new capabilities. Because transferred genes perform many medically important functions, such as conf...
Micah Hamady, M. D. Betterton, Rob Knight
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
16 years 3 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
ML
2002
ACM
123views Machine Learning» more  ML 2002»
15 years 2 months ago
Feature Generation Using General Constructor Functions
Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creatin...
Shaul Markovitch, Dan Rosenstein
111
Voted
ICML
2000
IEEE
16 years 3 months ago
A Nonparametric Approach to Noisy and Costly Optimization
This paper describes Pairwise Bisection: a nonparametric approach to optimizing a noisy function with few function evaluations. The algorithm uses nonparametric reasoning about si...
Brigham S. Anderson, Andrew W. Moore, David Cohn