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» Robust Learning - Rich and Poor
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ICML
2002
IEEE
15 years 10 months ago
Active + Semi-supervised Learning = Robust Multi-View Learning
In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view a...
Ion Muslea, Steven Minton, Craig A. Knoblock
ICML
2003
IEEE
15 years 10 months ago
Robust Induction of Process Models from Time-Series Data
Pat Langley, Dileep George, Stephen D. Bay, Kazumi...
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ICML
2008
IEEE
15 years 10 months ago
Extracting and composing robust features with denoising autoencoders
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
SOFSEM
1999
Springer
15 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
NCI
2004
142views Neural Networks» more  NCI 2004»
14 years 10 months ago
A competitive and cooperative learning approach to robust data clustering
This paper presents a new semi-competitive learning paradigm named Competitive and Cooperative Learning (CCL), in which seed points not only compete each other for updating to ada...
Yiu-ming Cheung