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TSMC
2008
128views more  TSMC 2008»
15 years 5 months ago
Adaptive Sensor Placement and Boundary Estimation for Monitoring Mass Objects
Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could...
Zhen Guo, MengChu Zhou, Guofei Jiang
FOCS
2002
IEEE
15 years 10 months ago
Learning Intersections and Thresholds of Halfspaces
We give the first polynomial time algorithm to learn any function of a constant number of halfspaces under the uniform distribution on the Boolean hypercube to within any constan...
Adam Klivans, Ryan O'Donnell, Rocco A. Servedio
FOIKS
2008
Springer
15 years 6 months ago
Cost-Minimising Strategies for Data Labelling: Optimal Stopping and Active Learning
Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X , given a training dataset D of pair...
Christos Dimitrakakis, Christian Savu-Krohn
ICML
2003
IEEE
16 years 6 months ago
Learning on the Test Data: Leveraging Unseen Features
This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and t...
Benjamin Taskar, Ming Fai Wong, Daphne Koller
NLPRS
2001
Springer
15 years 9 months ago
A Bayesian Approach to Semi-Supervised Learning
Recent research in automated learning has focused on algorithms that learn from a combination of tagged and untagged data. Such algorithms can be referred to as semi-supervised in...
Rebecca F. Bruce