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» Approximation Algorithms for Scheduling on Multiple Machines
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ICML
1999
IEEE
14 years 8 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
FCCM
1998
IEEE
169views VLSI» more  FCCM 1998»
13 years 12 months ago
Scalable Network Based FPGA Accelerators for an Automatic Target Recognition Application
Abstract Image processing, specifically Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) imagery, is an application area that can require tremendous processing ...
Ruth Sivilotti, Young Cho, Wen-King Su, Danny Cohe...
ICMLA
2010
13 years 5 months ago
Ensembles of Neural Networks for Robust Reinforcement Learning
Reinforcement learning algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, their traini...
Alexander Hans, Steffen Udluft
GLOBECOM
2008
IEEE
14 years 2 months ago
Performance Metric Sensitivity Computation for Optimization and Trade-Off Analysis in Wireless Networks
Abstract—We develop and evaluate a new method for estimating and optimizing various performance metrics for multihop wireless networks, including MANETs. We introduce an approxim...
John S. Baras, Vahid Tabatabaee, George Papageorgi...
IJCAI
2003
13 years 9 months ago
Employing Trainable String Similarity Metrics for Information Integration
The problem of identifying approximately duplicate objects in databases is an essential step for the information integration process. Most existing approaches have relied on gener...
Mikhail Bilenko, Raymond J. Mooney