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» Towards Using Fewer Features for Text Classification
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WEBI
2007
Springer
15 years 6 months ago
Correct your text with Google
With the increasing amount of text files that are produced nowadays, spell checkers have become essential tools for everyday tasks of millions of end users. Among the years, seve...
Stéphanie Jacquemont, François Jacqu...
CIKM
2010
Springer
14 years 9 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan
AUSAI
2007
Springer
15 years 3 months ago
Effectiveness of Methods for Syntactic and Semantic Recognition of Numeral Strings: Tradeoffs Between Number of Features and Len
Abstract. This paper describes and compares the use of methods based on Ngrams (specifically trigrams and pentagrams), together with five features, to recognise the syntactic and s...
Kyongho Min, William H. Wilson, Byeong Ho Kang
SIGKDD
2002
92views more  SIGKDD 2002»
14 years 11 months ago
A Machine Learning Approach for the Curation of Biomedical Literature - KDD Cup 2002 (Task 1)
In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental eviden...
S. Sathiya Keerthi, Chong Jin Ong, Keng Boon Siah,...
TIP
2002
179views more  TIP 2002»
14 years 11 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
Te-Won Lee, Michael S. Lewicki