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67
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NAACL
2003
14 years 11 months ago
Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
Shona Douglas
ICML
2009
IEEE
15 years 11 months ago
Learning from measurements in exponential families
Given a model family and a set of unlabeled examples, one could either label specific examples or state general constraints--both provide information about the desired model. In g...
Percy Liang, Michael I. Jordan, Dan Klein
89
Voted
ICONIP
2009
14 years 8 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
73
Voted
ICRA
2009
IEEE
125views Robotics» more  ICRA 2009»
15 years 4 months ago
A novel method for learning policies from constrained motion
— Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently...
Matthew Howard, Stefan Klanke, Michael Gienger, Ch...
PRL
2008
91views more  PRL 2008»
14 years 10 months ago
Fuzzy relevance vector machine for learning from unbalanced data and noise
Handing unbalanced data and noise are two important issues in the field of machine learning. This paper proposed a complete framework of fuzzy relevance vector machine by weightin...
Dingfang Li, Wenchao Hu, Wei Xiong, Jin-Bo Yang