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» Learning Mixtures of Gaussians
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ICML
2008
IEEE
16 years 7 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
COLT
2001
Springer
15 years 11 months ago
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we ...
Peter L. Bartlett, Shahar Mendelson
ICML
2010
IEEE
15 years 7 months ago
Gaussian Process Change Point Models
We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to loca...
Yunus Saatci, Ryan Turner, Carl Edward Rasmussen
SIGIR
2005
ACM
16 years 1 days ago
Using term informativeness for named entity detection
Informal communication (e-mail, bulletin boards) poses a difficult learning environment because traditional grammatical and lexical information are noisy. Other information is nec...
Jason D. M. Rennie, Tommi Jaakkola
ICML
2007
IEEE
16 years 7 months ago
Most likely heteroscedastic Gaussian process regression
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
Kristian Kersting, Christian Plagemann, Patrick Pf...