Near-Optimal Bayesian Active Learning with Noisy Observations

13 years 4 months ago

Download www.cs.caltech.edu

We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise

Daniel Golovin, Andreas Krause, Debajyoti Ray

Real-time Traffic

CORR 2010 | Education | Fundamental Problem | Greedy Algorithm | Observations |

claim paper

» Active Learning for Structure in Bayesian Networks

» Multichannel audio segmentation for continuous observation and archival of large spaces

» Learning Complex Concepts Using Crowdsourcing A Bayesian Approach

» Structural Learning of Activities from Sparse Datasets

» Cleaning and querying noisy sensors

» Bayesian recognition of motion related activities with inertial sensors

» A Minimum Relative Entropy Principle for Learning and Acting

» Unsupervised body scheme learning through selfperception

Post Info
More Details (n/a)

Added	09 Dec 2010
Updated	09 Dec 2010
Type	Journal
Year	2010
Where	CORR
Authors	Daniel Golovin, Andreas Krause, Debajyoti Ray

Comments (0)

Sciweavers

Near-Optimal Bayesian Active Learning with Noisy Observations

CORR 2010 | Education | Fundamental Problem | Greedy Algorithm | Observations |

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers