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126
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AMAI
2004
Springer
15 years 6 months ago
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Piotr J. Gmytrasiewicz, Prashant Doshi
ICRA
2002
IEEE
123views Robotics» more  ICRA 2002»
15 years 5 months ago
Sensor Fusion of Structure-from-Motion, Bathymetric 3D, and Beacon-Based Navigation Modalities
This paper describes an approach for the fusion of 3D data underwater obtained from multiple sensing modalities. In particular, we examine the combination of imagebased Structure-...
Hanumant Singh, Garbis Salgian, Ryan Eustice, Robe...
111
Voted
KDD
2010
ACM
245views Data Mining» more  KDD 2010»
15 years 2 months ago
Learning incoherent sparse and low-rank patterns from multiple tasks
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...
Jianhui Chen, Ji Liu, Jieping Ye
111
Voted
IOR
2008
91views more  IOR 2008»
15 years 22 days ago
A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains
We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random an...
Pierre L'Ecuyer, Christian Lécot, Bruno Tuf...
91
Voted
MOC
2000
105views more  MOC 2000»
15 years 15 days ago
On the iteratively regularized Gauss-Newton method for solving nonlinear ill-posed problems
The iteratively regularized Gauss-Newton method is applied to compute the stable solutions to nonlinear ill-posed problems F (x) = y when the data y is given approximately by y wit...
Jin Qi-nian