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» Evaluating learning algorithms and classifiers
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ILP
2004
Springer
15 years 10 months ago
Improving Rule Evaluation Using Multitask Learning
Abstract. This paper introduces Deft, a new multitask learning approach for rule learning algorithms. Like other multitask learning systems, the one proposed here is able to improv...
Mark D. Reid
WWW
2002
ACM
16 years 5 months ago
Using web structure for classifying and describing web pages
The structure of the web is increasingly being used to improve organization, search, and analysis of information on the web. For example, Google uses the text in citing documents ...
Eric J. Glover, Kostas Tsioutsiouliklis, Steve Law...
152
Voted
GECCO
2009
Springer
161views Optimization» more  GECCO 2009»
15 years 11 months ago
Are evolutionary rule learning algorithms appropriate for malware detection?
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...
149
Voted
HIS
2008
15 years 6 months ago
A Sequential Learning Resource Allocation Network for Image Processing Applications
Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a...
Stefan Wildermann, Jürgen Teich
153
Voted
KDD
2002
ACM
179views Data Mining» more  KDD 2002»
16 years 5 months ago
Combining clustering and co-training to enhance text classification using unlabelled data
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
Bhavani Raskutti, Herman L. Ferrá, Adam Kow...