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Machine Learning: ECML-98
Text categorization with Support Vector Machines: Learning with many relevant features
other
Author(s):
Thorsten Joachims
Publication date
(Online):
June 16 2005
Publisher:
Springer Berlin Heidelberg
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Recursive Rule based Visual Categorization
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Book Chapter
Publication date (Print):
1998
Publication date (Online):
June 16 2005
Pages
: 137-142
DOI:
10.1007/BFb0026683
SO-VID:
60735f7e-d0be-4723-9382-8dd104370225
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Book chapters
pp. 25
Part-of-speech tagging using decision trees
pp. 4
Naive (Bayes) at forty: The independence assumption in information retrieval
pp. 101
A monotonic measure for optimal feature selection
pp. 250
First-order learning for Web mining
pp. 292
Recursive lazy learning for modeling and control
pp. 95
Feature subset selection in text-learning
pp. 119
God doesn't always shave with Occam's razor — Learning when and how to prune
pp. 131
Pruning decision trees with misclassification costs
pp. 137
Text categorization with Support Vector Machines: Learning with many relevant features
pp. 160
Improved pairwise coupling classification with correcting classifiers
pp. 382
Theoretical results on reinforcement learning with temporally abstract options
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