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AAAI
1998
13 years 8 months ago
Applying Online Search Techniques to Continuous-State Reinforcement Learning
In this paper, we describe methods for e ciently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boo...
Scott Davies, Andrew Y. Ng, Andrew W. Moore
JMLR
2010
137views more  JMLR 2010»
13 years 2 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
SIGCOMM
2006
ACM
14 years 1 months ago
Beyond bloom filters: from approximate membership checks to approximate state machines
Many networking applications require fast state lookups in a concurrent state machine, which tracks the state of a large number of flows simultaneously. We consider the question ...
Flavio Bonomi, Michael Mitzenmacher, Rina Panigrah...
DASFAA
2011
IEEE
525views Database» more  DASFAA 2011»
12 years 11 months ago
StreamFitter: A Real Time Linear Regression Analysis System for Continuous Data Streams
In this demo, we present the StreamFitter system for real-time regression analysis on continuous data streams. In order to perform regression on data streams, it is necessary to co...
Chandima H. Nadungodage, Yuni Xia, Fang Li, Jaehwa...
UAI
2004
13 years 8 months ago
Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials c...
Barry R. Cobb, Prakash P. Shenoy