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» On learning algorithm selection for classification
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ICML
2005
IEEE
14 years 9 months ago
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning me...
Yushi Jing, Vladimir Pavlovic, James M. Rehg
ICDM
2007
IEEE
138views Data Mining» more  ICDM 2007»
14 years 3 months ago
Bandit-Based Algorithms for Budgeted Learning
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
ICML
2010
IEEE
13 years 10 months ago
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the m...
Han-Hsing Tu, Hsuan-Tien Lin
ROBIO
2006
IEEE
153views Robotics» more  ROBIO 2006»
14 years 2 months ago
GA-based Feature Subset Selection for Myoelectric Classification
– This paper presents an ongoing investigation to select optimal subset of features from set of well-known myoelectric signals (MES) features in time and frequency domains. Four ...
Mohammadreza Asghari Oskoei, Huosheng Hu
CIDM
2009
IEEE
14 years 3 months ago
Ensemble member selection using multi-objective optimization
— Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ens...
Tuve Löfström, Ulf Johansson, Henrik Bos...