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» On learning algorithm selection for classification
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IJON
2002
85views more  IJON 2002»
15 years 6 months ago
Learning statistically efficient features for speaker recognition
We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for a speaker. The basis functions learned by the algori...
Gil-Jin Jang, Te-Won Lee, Yung-Hwan Oh
ICML
2005
IEEE
16 years 7 months ago
Building Sparse Large Margin Classifiers
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Bernhard Schölkopf, Gökhan H. Bakir, Min...
GECCO
2007
Springer
210views Optimization» more  GECCO 2007»
16 years 24 days ago
Mining breast cancer data with XCS
In this paper, we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a prima...
Faten Kharbat, Larry Bull, Mohammed Odeh
JMLR
2008
139views more  JMLR 2008»
15 years 6 months ago
Regularization on Graphs with Function-adapted Diffusion Processes
Harmonic analysis and diffusion on discrete data has been shown to lead to state-of-theart algorithms for machine learning tasks, especially in the context of semi-supervised and ...
Arthur D. Szlam, Mauro Maggioni, Ronald R. Coifman
NIPS
2007
15 years 8 months ago
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta
Rosetta is one of the leading algorithms for protein structure prediction today. It is a Monte Carlo energy minimization method requiring many random restarts to find structures ...
Ben Blum, Michael I. Jordan, David Kim, Rhiju Das,...