Stochastic m-estimators: controlling accuracy-cost tradeoffs in machine learning

Stochastic m-estimators: controlling accuracy-cost tradeoffs in machine learning

m-Estimation represents a broad class of estimators, including least-squares and maximum likelihood, and is a widely used tool for statistical inference. Its successful application however, often requires negotiating physical resources for desired levels of accuracy. These limiting factors, which we...

Full description

Bibliographic Details
Main Author: Dillon, Joshua V.
Published: Georgia Institute of Technology 2012
Subjects:
Approximate inference
Semi-supervised learning
Parameter learning
Structured prediction
Graphical models
Machine learning
Computational complexity
Markov random fields
Online Access:http://hdl.handle.net/1853/42913

Internet

http://hdl.handle.net/1853/42913

Similar Items