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» A Modelling Framework for Functional Imagination
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TCOM
2010
112views more  TCOM 2010»
14 years 4 months ago
Concise Derivation of Scattering Function from Channel Entropy Maximization
In order to provide a concise time-varying SISO channel model, the principle of maximum entropy is applied to scattering function derivation. The resulting model is driven by few p...
François-Xavier Socheleau, Christophe Laot,...
VLDB
2007
ACM
147views Database» more  VLDB 2007»
15 years 9 months ago
A General Framework for Modeling and Processing Optimization Queries
An optimization query asks for one or more data objects that maximize or minimize some function over the data set. We propose a general class of queries, model-based optimization ...
Michael Gibas, Ning Zheng, Hakan Ferhatosmanoglu
TROB
2002
116views more  TROB 2002»
14 years 9 months ago
Visual servoing via navigation functions
Abstract-- This paper presents a framework for visual servoing that guarantees convergence to a visible goal from almost every initially visible configuration while maintaining ful...
Noah J. Cowan, Joel Weingarten, Daniel E. Koditsch...
QEST
2009
IEEE
15 years 4 months ago
Integrating TPNs and Performance Bound Techniques in ITPN-PerfBound: A New Import Functionality
Abstract—ITPN-PerfBound is a graphical tool for the modeling and performance bound analysis of Interval Time Petri Nets (ITPN), that has been developed within the DrawNET modelin...
Elina Pacini Naumovich, Simona Bernardi
ICML
2005
IEEE
15 years 10 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore