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PKDD
2010
Springer
212views Data Mining» more  PKDD 2010»
15 years 2 months ago
Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning
Abstract. One solution to the lack of label problem is to exploit transfer learning, whereby one acquires knowledge from source-domains to improve the learning performance in the t...
ErHeng Zhong, Wei Fan, Qiang Yang, Olivier Versche...
KDD
2002
ACM
157views Data Mining» more  KDD 2002»
16 years 4 months ago
Learning nonstationary models of normal network traffic for detecting novel attacks
Traditional intrusion detection systems (IDS) detect attacks by comparing current behavior to signatures of known attacks. One main drawback is the inability of detecting new atta...
Matthew V. Mahoney, Philip K. Chan
ML
2000
ACM
124views Machine Learning» more  ML 2000»
15 years 3 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...
ICANN
2009
Springer
15 years 10 months ago
Selective Attention Improves Learning
Abstract. We demonstrate that selective attention can improve learning. Considerably fewer samples are needed to learn a source separation problem when the inputs are pre-segmented...
Antti Yli-Krekola, Jaakko Särelä, Harri ...
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
15 years 11 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman