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KDD
1994
ACM
140views Data Mining» more  KDD 1994»
14 years 26 days ago
A Comparison of Pruning Methods for Relational Concept Learning
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are m...
Johannes Fürnkranz
SOFSEM
1999
Springer
14 years 1 months ago
Coherent Concepts, Robust Learning
We study learning scenarios in which multiple learners are involved and “nature” imposes some constraints that force the predictions of these learners to behave coherently. Thi...
Dan Roth, Dmitry Zelenko
INCDM
2010
Springer
138views Data Mining» more  INCDM 2010»
13 years 7 months ago
Learning Discriminative Distance Functions for Case Retrieval and Decision Support
The importance of learning distance functions is gradually being acknowledged by the machine learning community, and different techniques are suggested that can successfully learn ...
Alexey Tsymbal, Martin Huber, Shaohua Kevin Zhou
IJON
2002
98views more  IJON 2002»
13 years 8 months ago
Blind deconvolution by simple adaptive activation function neuron
The `Bussgang' algorithm is one among the most known blind deconvolution techniques in the adaptive signal processing literature. It relies on a Bayesian estimator of the sou...
Simone Fiori
COLT
2001
Springer
14 years 1 months ago
Agnostic Boosting
We prove strong noise-tolerance properties of a potential-based boosting algorithm, similar to MadaBoost (Domingo and Watanabe, 2000) and SmoothBoost (Servedio, 2003). Our analysi...
Shai Ben-David, Philip M. Long, Yishay Mansour