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ICML
2010
IEEE
13 years 9 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
ECML
2006
Springer
14 years 1 days ago
Transductive Gaussian Process Regression with Automatic Model Selection
Abstract. In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at ...
Quoc V. Le, Alexander J. Smola, Thomas Gärtne...
WSC
2008
13 years 10 months ago
Distributed multi-layered workload synthesis for testing stream processing systems
Testing and benchmarking of stream processing systems requires workload representative of real world scenarios with myriad of users, interacting through different applications ove...
Eric Bouillet, Parijat Dube, David George, Zhen Li...
IJCAI
2007
13 years 9 months ago
Collapsed Variational Dirichlet Process Mixture Models
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of ...
Kenichi Kurihara, Max Welling, Yee Whye Teh
ER
2004
Springer
107views Database» more  ER 2004»
14 years 1 months ago
A Scaleless Data Model for Direct and Progressive Spatial Query Processing
A progressive spatial query retrieves spatial data based on previous queries (e.g., to fetch data in a more restricted area with higher resolution). A direct query, on the other si...
Sai Sun, Sham Prasher, Xiaofang Zhou