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» Evaluating learning algorithms and classifiers
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KDD
2006
ACM
180views Data Mining» more  KDD 2006»
16 years 4 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
129
Voted
SIGIR
2004
ACM
15 years 9 months ago
Classifying racist texts using a support vector machine
In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system for the PRINCIP project which sets out to automatically classi...
Edel Greevy, Alan F. Smeaton
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
16 years 4 months ago
Effective multi-label active learning for text classification
Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical ...
Bishan Yang, Jian-Tao Sun, Tengjiao Wang, Zheng Ch...
142
Voted
KDD
1995
ACM
109views Data Mining» more  KDD 1995»
15 years 7 months ago
An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers
The Bayesianclassifier is a simple approachto classification that producesresults that are easy for people to interpret. In many cases, the Bayesianclassifieris at leastasaccurate...
Michael J. Pazzani
208
Voted
IWCLS
2007
Springer
15 years 10 months ago
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most c...
Larry Bull