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» Infinite Ensemble Learning with Support Vector Machines
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ICML
2005
IEEE
16 years 2 months ago
Dynamic preferences in multi-criteria reinforcement learning
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Sriraam Natarajan, Prasad Tadepalli
ICPR
2004
IEEE
16 years 3 months ago
Sequence Recognition with Scanning N-Tuple Ensembles
The Scanning N-Tuple classifier (SNT) is a fast and accurate method for classifying sequences. Applications include both on-line and off-line hand-written character recognition. S...
Simon M. Lucas, Tzu-Kuo Huang
IJCNN
2006
IEEE
15 years 8 months ago
Pattern Selection for Support Vector Regression based on Sparseness and Variability
— Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubi...
Jiyoung Sun, Sungzoon Cho
BMCBI
2008
88views more  BMCBI 2008»
15 years 2 months ago
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Na
Myron Peto, Andrzej Kloczkowski, Vasant Honavar, R...
AHS
2007
IEEE
219views Hardware» more  AHS 2007»
15 years 8 months ago
A learning machine for resource-limited adaptive hardware
Machine Learning algorithms allow to create highly adaptable systems, since their functionality only depends on the features of the inputs and the coefficients found during the tr...
Davide Anguita, Alessandro Ghio, Stefano Pischiutt...