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AI
2004
Springer
15 years 6 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and imp...
Huan Liu, Hiroshi Motoda, Lei Yu
CVPR
2010
IEEE
15 years 4 months ago
P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the ...
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
ICDE
2011
IEEE
217views Database» more  ICDE 2011»
14 years 10 months ago
Partitioning techniques for fine-grained indexing
— Many data-intensive websites use databases that grow much faster than the rate that users access the data. Such growing datasets lead to ever-increasing space and performance o...
Eugene Wu 0002, Samuel Madden
RECSYS
2009
ACM
16 years 1 months ago
Rate it again: increasing recommendation accuracy by user re-rating
A common approach to designing Recommender Systems (RS) consists of asking users to explicitly rate items in order to collect feedback about their preferences. However, users have...
Xavier Amatriain, Josep M. Pujol, Nava Tintarev, N...
IROS
2008
IEEE
121views Robotics» more  IROS 2008»
16 years 1 months ago
Learning robot motion control with demonstration and advice-operators
Abstract— As robots become more commonplace within society, the need for tools to enable non-robotics-experts to develop control algorithms, or policies, will increase. Learning ...
Brenna Argall, Brett Browning, Manuela M. Veloso