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» Hedging predictions in machine learning
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ICML
2008
IEEE
16 years 5 months ago
Detecting statistical interactions with additive groves of trees
Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be expressed as the sum of lower-d...
Daria Sorokina, Rich Caruana, Mirek Riedewald, Dan...
ICML
2008
IEEE
16 years 5 months ago
Gaussian process product models for nonparametric nonstationarity
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
Ryan Prescott Adams, Oliver Stegle
ICML
2006
IEEE
16 years 5 months ago
Dynamic topic models
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...
David M. Blei, John D. Lafferty
ICML
2006
IEEE
16 years 5 months ago
Efficient co-regularised least squares regression
In many applications, unlabelled examples are inexpensive and easy to obtain. Semisupervised approaches try to utilise such examples to reduce the predictive error. In this paper,...
Stefan Wrobel, Thomas Gärtner, Tobias Scheffe...
ICML
2003
IEEE
16 years 5 months ago
Regression Error Characteristic Curves
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generaliz...
Jinbo Bi, Kristin P. Bennett