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ICPR
2004
IEEE
14 years 9 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
ICALT
2006
IEEE
14 years 1 months ago
Adaptive e-Learning Methods and IMS Learning Design: An Integrated Approach
This position paper shows how several classical methods in adaptive learning can be addressed using IMS Learning Design. After a definition of four main questions to classify adap...
Daniel Burgos, Marcus Specht
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
14 years 8 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
MCS
2010
Springer
14 years 2 months ago
Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles
A method for applying weighted decoding to error-correcting output code ensembles of binary classifiers is presented. This method is sensitive to the target class in that a separa...
Raymond S. Smith, Terry Windeatt
NPL
2000
95views more  NPL 2000»
13 years 7 months ago
Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping
Generative topographic mapping (GTM) is a statistical model to extract a hidden smooth manifold from data, like the self-organizing map (SOM). Although a deterministic search algo...
Akio Utsugi