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» On Combining Classifiers
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BMCBI
2006
173views more  BMCBI 2006»
13 years 9 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen
ISBI
2006
IEEE
14 years 9 months ago
Nonlinear classification of EEG data for seizure detection
We address the problem of classification of EEG recordings for the detection of epileptic seizures. We assume that the EEG measurements can be described by a low dimensional manif...
Mabel Ramírez-Vélez, Richard Staba, ...
ICIAP
2009
ACM
14 years 9 months ago
Multi-class Binary Symbol Classification with Circular Blurred Shape Models
Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions...
Sergio Escalera, Alicia Fornés, Oriol Pujol...
PR
2008
120views more  PR 2008»
13 years 8 months ago
Fuzzy integral based information fusion for classification of highly confusable non-speech sounds
Acoustic event classification may help to describe acoustic scenes and contribute to improve the robustness of speech technologies. In this work, fusion of different information s...
Andrey Temko, Dusan Macho, Climent Nadeu
ICML
2004
IEEE
14 years 9 months ago
Multiple kernel learning, conic duality, and the SMO algorithm
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...