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» Uncertainty Based Selection of Learning Experiences
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ICPR
2004
IEEE
14 years 8 months ago
Selective Sampling Based on the Variation in Label Assignments
In this paper, a new selective sampling method for the active learning framework is presented. Initially, a small training set ? and a large unlabeled set ? are given. The goal is...
Piotr Juszczak, Robert P. W. Duin
DASFAA
2008
IEEE
116views Database» more  DASFAA 2008»
13 years 9 months ago
MBR Models for Uncertainty Regions of Moving Objects
The increase in the advanced location based services such as traffic coordination and management necessitates the need for advanced models tracking the positions of Moving Objects ...
Shayma Alkobaisi, Wan D. Bae, Seon Ho Kim, Byunggu...
IJRR
2011
130views more  IJRR 2011»
12 years 11 months ago
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
— This paper presents LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will...
Jur van den Berg, Pieter Abbeel, Ken Goldberg
AI
2004
Springer
13 years 7 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
NIPS
2003
13 years 8 months ago
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing c...
Yoshua Bengio, Yves Grandvalet