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ICML
2002
IEEE
16 years 5 months ago
Learning to Share Distributed Probabilistic Beliefs
In this paper, we present a general machine learning approach to the problem of deciding when to share probabilistic beliefs between agents for distributed monitoring. Our approac...
Christopher Leckie, Kotagiri Ramamohanarao
SSPR
2004
Springer
15 years 9 months ago
Learning from General Label Constraints
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and i...
Tijl De Bie, Johan A. K. Suykens, Bart De Moor
ICANN
2009
Springer
15 years 9 months ago
Learning SVMs from Sloppily Labeled Data
This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labe...
Guillaume Stempfel, Liva Ralaivola
ECML
2001
Springer
15 years 9 months ago
A Framework for Learning Rules from Multiple Instance Data
Abstract. This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is r...
Yann Chevaleyre, Jean-Daniel Zucker
ECML
2007
Springer
15 years 8 months ago
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble
Abstract. Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the deci...
Anneleen Van Assche, Hendrik Blockeel