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» Optimal feature selection for support vector machines
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GECCO
2007
Springer
180views Optimization» more  GECCO 2007»
13 years 11 months ago
Support vector regression for classifier prediction
In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifie...
Daniele Loiacono, Andrea Marelli, Pier Luca Lanzi
FSKD
2007
Springer
98views Fuzzy Logic» more  FSKD 2007»
14 years 1 months ago
Learning Selective Averaged One-Dependence Estimators for Probability Estimation
Naïve Bayes is a well-known effective and efficient classification algorithm, but its probability estimation performance is poor. Averaged One-Dependence Estimators, simply AODE,...
Qing Wang, Chuan-hua Zhou, Jiankui Guo
IWINAC
2009
Springer
14 years 11 days ago
Results of an Adaboost Approach on Alzheimer's Disease Detection on MRI
Abstract. In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide the feature extraction processes for the detection of Alzheimer's d...
Alexandre Savio, Maite García-Sebasti&aacut...
LREC
2010
157views Education» more  LREC 2010»
13 years 9 months ago
Is Sentiment a Property of Synsets? Evaluating Resources for Sentiment Classification using Machine Learning
Existing approaches to classifying documents by sentiment include machine learning with features created from n-grams and part of speech. This paper explores a different approach ...
Aleksander Wawer
JMLR
2006
89views more  JMLR 2006»
13 years 7 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel