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MP
2006
87views more  MP 2006»
13 years 7 months ago
Convexity and decomposition of mean-risk stochastic programs
Abstract. Traditional stochastic programming is risk neutral in the sense that it is concerned with the optimization of an expectation criterion. A common approach to addressing ri...
Shabbir Ahmed
ICRA
2002
IEEE
183views Robotics» more  ICRA 2002»
14 years 19 days ago
Mobile Robot Localization Based on Random Closed Set Model Maps
In this paper we present a novel approach to mobile robot navigation in natural unstructured environments. Natural scenes can be considered as random fields where a large number ...
Stefan Rolfes, Maria-João Rendas
CDC
2009
IEEE
106views Control Systems» more  CDC 2009»
14 years 13 days ago
Gradient methods for iterative distributed control synthesis
— In this paper we present a gradient method to iteratively update local controllers of a distributed linear system driven by stochastic disturbances. The control objective is to...
Karl Martensson, Anders Rantzer
TASLP
2002
109views more  TASLP 2002»
13 years 7 months ago
Particle methods for Bayesian modeling and enhancement of speech signals
This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution mod...
Jaco Vermaak, Christophe Andrieu, Arnaud Doucet, S...
NIPS
2008
13 years 9 months ago
Bayesian Kernel Shaping for Learning Control
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakum...