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» Structured Representation of Complex Stochastic Systems
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QEST
2006
IEEE
14 years 1 months ago
Bound-Preserving Composition for Markov Reward Models
Stochastic orders can be applied to Markov reward models and used to aggregate models, while introducing a bounded error. Aggregation reduces the number of states in a model, miti...
David Daly, Peter Buchholz, William H. Sanders
JAIR
2002
101views more  JAIR 2002»
13 years 7 months ago
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of ...
Eugenio Di Sciascio, Francesco M. Donini, Marina M...
ICML
2009
IEEE
14 years 8 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
GLOBECOM
2007
IEEE
14 years 2 months ago
Reduced Complexity Sphere Decoding for Square QAM via a New Lattice Representation
— Sphere decoding (SD) is a low complexity maximum likelihood (ML) detection algorithm, which has been adapted for different linear channels in digital communications. The comple...
Luay Azzam, Ender Ayanoglu
ASPDAC
2007
ACM
146views Hardware» more  ASPDAC 2007»
13 years 11 months ago
Practical Implementation of Stochastic Parameterized Model Order Reduction via Hermite Polynomial Chaos
Abstract-- This paper describes the stochastic model order reduction algorithm via stochastic Hermite Polynomials from the practical implementation perspective. Comparing with exis...
Yi Zou, Yici Cai, Qiang Zhou, Xianlong Hong, Sheld...