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» Evaluating Feature Selection for SVMs in High Dimensions
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112
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BMCBI
2010
224views more  BMCBI 2010»
14 years 10 months ago
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Susmita Datta, Vasyl Pihur, Somnath Datta
85
Voted
ICML
2004
IEEE
15 years 11 months ago
Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
Rong Jin, Huan Liu
113
Voted
SPEECH
2011
14 years 5 months ago
Discrimination of speech from nonspeeech in broadcast news based on modulation frequency features
We describe a content based speech discrimination algorithm in broadcast news based on the time-varying information provided by the modulation spectrum. Due to the varying degrees...
Maria E. Markaki, Yannis Stylianou
ICMLA
2008
14 years 11 months ago
Highly Scalable SVM Modeling with Random Granulation for Spam Sender Detection
Spam sender detection based on email subject data is a complex large-scale text mining task. The dataset consists of email subject lines and the corresponding IP address of the em...
Yuchun Tang, Yuanchen He, Sven Krasser
PKDD
2009
Springer
148views Data Mining» more  PKDD 2009»
15 years 4 months ago
Feature Selection by Transfer Learning with Linear Regularized Models
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...
Thibault Helleputte, Pierre Dupont