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IBPRIA
2005
Springer
15 years 8 months ago
Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning
A natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noi...
Iván Cantador, José R. Dorronsoro
PR
2007
81views more  PR 2007»
15 years 2 months ago
Graph embedding using tree edit-union
In this paper we address the problem of how to learn a structural prototype that can be used to represent the variations present in a set of trees. The prototype serves as a patte...
Andrea Torsello, Edwin R. Hancock
EUSFLAT
2009
125views Fuzzy Logic» more  EUSFLAT 2009»
15 years 1 months ago
A Dynamic Classification Method for the Discrimination of Evolving Data
Classes issued of evolving systems are dynamic and their characteristics vary over the time. Assigning a pattern to a class is achieved using a classifier. Therefore, the classifie...
Laurent Hartert, Moamar Sayed Mouchaweh, Patrice B...
158
Voted
KDD
2006
ACM
174views Data Mining» more  KDD 2006»
16 years 3 months ago
Onboard classifiers for science event detection on a remote sensing spacecraft
Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a space...
Ashley Davies, Benjamin Cichy, Dominic Mazzoni, Ng...
SIGIR
2006
ACM
15 years 9 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi