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» Hierarchical approximation and localization
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153
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ICML
1999
IEEE
16 years 5 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
216
Voted
POPL
2002
ACM
16 years 4 months ago
An efficient profile-analysis framework for data-layout optimizations
Data-layout optimizations rearrange fields within objects, objects within objects, and objects within the heap, with the goal of increasing spatial locality. While the importance ...
Rastislav Bodík, Shai Rubin, Trishul M. Chi...
143
Voted
OSDI
2008
ACM
16 years 4 months ago
Probabilistic Inference in Queueing Networks
Although queueing models have long been used to model the performance of computer systems, they are out of favor with practitioners, because they have a reputation for requiring u...
Charles A. Sutton, Michael I. Jordan
EDBT
2004
ACM
142views Database» more  EDBT 2004»
16 years 4 months ago
Iterative Incremental Clustering of Time Series
We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property...
Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dim...
EDBT
2006
ACM
136views Database» more  EDBT 2006»
16 years 4 months ago
Optimizing Monitoring Queries over Distributed Data
Scientific data in the life sciences is distributed over various independent multi-format databases and is constantly expanding. We discuss a scenario where a life science research...
Frank Neven, Dieter Van de Craen