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PAMI
2006
143views more  PAMI 2006»
13 years 7 months ago
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
In this paper we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a ...
Shihao Ji, Balaji Krishnapuram, Lawrence Carin
PAKDD
2011
ACM
473views Data Mining» more  PAKDD 2011»
13 years 1 months ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is pr...
Timothy Hospedales, Shaogang Gong and Tao Xiang
ECML
2005
Springer
14 years 1 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
MTA
2006
173views more  MTA 2006»
13 years 7 months ago
Active learning in very large databases
Abstract. Query-by-example and query-by-keyword both suffer from the problem of "aliasing," meaning that example-images and keywords potentially have variable interpretat...
Navneet Panda, Kingshy Goh, Edward Y. Chang
SDM
2008
SIAM
144views Data Mining» more  SDM 2008»
13 years 9 months ago
Active Learning with Model Selection in Linear Regression
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
Masashi Sugiyama, Neil Rubens