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ICPR
2008
IEEE
16 years 3 months ago
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
ICTAI
2008
IEEE
15 years 8 months ago
Using Imputation Techniques to Help Learn Accurate Classifiers
It is difficult to learn good classifiers when training data is missing attribute values. Conventional techniques for dealing with such omissions, such as mean imputation, general...
Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greine...
CHI
2009
ACM
16 years 2 months ago
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining ...
Justin Talbot, Bongshin Lee, Ashish Kapoor, Desney...
CVPR
2003
IEEE
16 years 4 months ago
Learning a discriminative classifier using shape context distances
For purpose of object recognition, we learn one discriminative classifier based on one prototype, using shape context distances as the feature vector. From multiple prototypes, th...
Hao Zhang 0003, Jitendra Malik
95
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CEC
2007
IEEE
15 years 6 months ago
Support vector machines for computing action mappings in learning classifier systems
XCS with Computed Action, briefly XCSCA, is a recent extension of XCS to tackle problems involving a large number of discrete actions. In XCSCA the classifier action is computed wi...
Daniele Loiacono, Andrea Marelli, Pier Luca Lanzi