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» Evaluating learning algorithms and classifiers
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AH
2004
Springer
15 years 8 months ago
Evaluating Information Filtering Techniques in an Adaptive Recommender System
Abstract. With the huge increase in the volume of information available in digital form and the increasing diversity of Web applications, the need for efficient, reliable, informat...
John O'Donovan, John Dunnion
MCS
2005
Springer
15 years 10 months ago
Ensemble Confidence Estimates Posterior Probability
We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribut...
Michael Muhlbaier, Apostolos Topalis, Robi Polikar
CIVR
2008
Springer
125views Image Analysis» more  CIVR 2008»
15 years 6 months ago
(Un)Reliability of video concept detection
Great effort has been made to improve video concept detection and continuous progress has been reported. With the current evaluation method being confined to carefully annotated d...
Jun Yang 0003, Alexander G. Hauptmann
COLCOM
2005
IEEE
15 years 10 months ago
An experimental evaluation of spam filter performance and robustness against attack
— In this paper, we show experimentally that learning filters are able to classify large corpora of spam and legitimate email messages with a high degree of accuracy. The corpor...
Steve Webb, Subramanyam Chitti, Calton Pu
IJCAI
1989
15 years 5 months ago
Noise-Tolerant Instance-Based Learning Algorithms
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning application...
David W. Aha, Dennis F. Kibler