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106
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NIPS
2004
15 years 3 months ago
Semi-supervised Learning by Entropy Minimization
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
Yves Grandvalet, Yoshua Bengio
106
Voted
CATA
2000
15 years 3 months ago
Neocognitron for rotated pattern recognition
Ideally computer pattern recognition systems should be insensitive to scaling, translation, distortion and rotation. Many neural network models have been proposed to address this ...
Michael Tran, Siddheswar Ray, Ronald Pose
ICASSP
2010
IEEE
15 years 2 months ago
Hierarchical dictionary learning for invariant classification
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
Leah Bar, Guillermo Sapiro
EMNLP
2010
14 years 12 months ago
Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing
Inducing a grammar directly from text is one of the oldest and most challenging tasks in Computational Linguistics. Significant progress has been made for inducing dependency gram...
Phil Blunsom, Trevor Cohn
ICANN
2011
Springer
14 years 5 months ago
Learning from Multiple Annotators with Gaussian Processes
Abstract. In many supervised learning tasks it can be costly or infeasible to obtain objective, reliable labels. We may, however, be able to obtain a large number of subjective, po...
Perry Groot, Adriana Birlutiu, Tom Heskes