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» Decisive Markov Chains
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ML
2002
ACM
246views Machine Learning» more  ML 2002»
15 years 1 months ago
Bayesian Clustering by Dynamics
This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to disc...
Marco Ramoni, Paola Sebastiani, Paul R. Cohen
CORR
2010
Springer
171views Education» more  CORR 2010»
14 years 8 months ago
Online Learning in Opportunistic Spectrum Access: A Restless Bandit Approach
We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activ...
Cem Tekin, Mingyan Liu
SIGMOD
2011
ACM
250views Database» more  SIGMOD 2011»
14 years 4 months ago
Hybrid in-database inference for declarative information extraction
In the database community, work on information extraction (IE) has centered on two themes: how to effectively manage IE tasks, and how to manage the uncertainties that arise in th...
Daisy Zhe Wang, Michael J. Franklin, Minos N. Garo...
MSWIM
2003
ACM
15 years 7 months ago
Markov-based modeling of wireless local area networks
Errors introduced by a wireless medium are more frequent and profound than contemporary wired media. Some of these errors, which are not corrected by the physical layer, result in...
Syed A. Khayam, Hayder Radha
WWW
2005
ACM
16 years 2 months ago
Executing incoherency bounded continuous queries at web data aggregators
Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some ...
Rajeev Gupta, Ashish Puri, Krithi Ramamritham