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» Machine Learning with Data Dependent Hypothesis Classes
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BMCBI
2006
158views more  BMCBI 2006»
13 years 8 months ago
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...
Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D...
COLT
2003
Springer
14 years 1 months ago
Maximum Margin Algorithms with Boolean Kernels
Recent work has introduced Boolean kernels with which one can learn linear threshold functions over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ...
Roni Khardon, Rocco A. Servedio
PVM
2009
Springer
14 years 3 months ago
Optimizing MPI Runtime Parameter Settings by Using Machine Learning
Abstract. Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for ...
Simone Pellegrini, Jie Wang, Thomas Fahringer, Han...
ICML
2005
IEEE
14 years 9 months ago
A martingale framework for concept change detection in time-varying data streams
In a data streaming setting, data points are observed one by one. The concepts to be learned from the data points may change infinitely often as the data is streaming. In this pap...
Shen-Shyang Ho
IJON
2008
173views more  IJON 2008»
13 years 8 months ago
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitabl...
Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li