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» Tackling Large State Spaces in Performance Modelling
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CSDA
2006
191views more  CSDA 2006»
13 years 9 months ago
Forecasting daily time series using periodic unobserved components time series models
We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend, seasonal and irregu...
Siem Jan Koopman, Marius Ooms
KR
1992
Springer
14 years 28 days ago
Conversational Events and Discourse State Change: A Preliminary Report
I argue that an action-based model of belief update is largely compatible with the proposals advanced in the literature on formal approaches to discourse interpretation, especiall...
Massimo Poesio
ATAL
2010
Springer
13 years 10 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
HCI
2009
13 years 6 months ago
Emotion Detection: Application of the Valence Arousal Space for Rapid Biological Usability Testing to Enhance Universal Access
Emotion is an important mental and physiological state, influencing cognition, perception, learning, communication, decision making, etc. It is considered as a definitive important...
Christian Stickel, Martin Ebner, Silke Steinbach-N...
ICPPW
2002
IEEE
14 years 1 months ago
SNOW: Software Systems for Process Migration in High-Performance, Heterogeneous Distributed Environments
This paper reports our experiences on the Scalable Network Of Workstation (SNOW) project, which implements a novel methodology to support user-level process migration for traditio...
Kasidit Chanchio, Xian-He Sun