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» On learning algorithm selection for classification
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ISDA
2009
IEEE
16 years 1 months ago
Measures for Unsupervised Fuzzy-Rough Feature Selection
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors t...
Neil MacParthalain, Richard Jensen
NIPS
2007
15 years 7 months ago
Discriminative Batch Mode Active Learning
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Most previous studies in active learning...
Yuhong Guo, Dale Schuurmans
ML
2000
ACM
124views Machine Learning» more  ML 2000»
15 years 6 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
KDD
2002
ACM
160views Data Mining» more  KDD 2002»
16 years 6 months ago
Scaling multi-class support vector machines using inter-class confusion
Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative m...
Shantanu Godbole, Sunita Sarawagi, Soumen Chakraba...
ECAI
2010
Springer
15 years 3 months ago
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space
The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket ...
Qiang Lou, Zoran Obradovic