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CIA
2006
Springer
15 years 5 months ago
Learning to Negotiate Optimally in Non-stationary Environments
Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
Vidya Narayanan, Nicholas R. Jennings
COLT
2006
Springer
15 years 5 months ago
Memory-Limited U-Shaped Learning
U-shaped learning is a learning behaviour in which the learner first learns a given target behaviour, then unlearns it and finally relearns it. Such a behaviour, observed by psych...
Lorenzo Carlucci, John Case, Sanjay Jain, Frank St...
ICMCS
2007
IEEE
180views Multimedia» more  ICMCS 2007»
15 years 8 months ago
Discrete Regularization for Perceptual Image Segmentation via Semi-Supervised Learning and Optimal Control
In this paper, we present a regularization approach on discrete graph spaces for perceptual image segmentation via semisupervised learning. In this approach, first, a spectral cl...
Hongwei Zheng, Olaf Hellwich
NIPS
2008
15 years 3 months ago
Optimization on a Budget: A Reinforcement Learning Approach
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-based "controllers" that modulate the behavior of the optimizer during ...
Paul Ruvolo, Ian R. Fasel, Javier R. Movellan
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 1 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh