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ICML
2008
IEEE
15 years 10 months ago
Empirical Bernstein stopping
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
Csaba Szepesvári, Jean-Yves Audibert, Volod...
ICIAP
2007
ACM
15 years 10 months ago
An information theoretic rule for sample size adaptation in particle filtering
To become robust, a tracking algorithm must be able to support uncertainty and ambiguity often inherently present in the data in form of occlusion and clutter. This comes usually ...
Oswald Lanz
BSN
2009
IEEE
140views Sensor Networks» more  BSN 2009»
15 years 4 months ago
A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks
—Human movement models often divide movements into parts. In walking the stride can be segmented into four different parts, and in golf and other sports, the swing is divided int...
Eric Guenterberg, Hassan Ghasemzadeh, Roozbeh Jafa...
DASFAA
2009
IEEE
152views Database» more  DASFAA 2009»
15 years 4 months ago
Predicting Timing Failures in Web Services
Abstract. Web services are increasingly being used in business critical environments, enabling uniform access to services provided by distinct parties. In these environments, an op...
Nuno Laranjeiro, Marco Vieira, Henrique Madeira
HICSS
2009
IEEE
104views Biometrics» more  HICSS 2009»
15 years 4 months ago
Celebrating Diversity in Volunteer Computing
The computing resources in a volunteer computing system are highly diverse in terms of software and hardware type, speed, availability, reliability, network connectivity, and othe...
David P. Anderson, Kevin Reed