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» Modeling GA Performance for Control Parameter Optimization
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NIPS
1998
15 years 3 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
IDA
2003
Springer
15 years 7 months ago
Very Predictive Ngrams for Space-Limited Probabilistic Models
In sequential prediction tasks, one repeatedly tries to predict the next element in a sequence. A classical way to solve these problems is to fit an order-n Markov model to the da...
Paul R. Cohen, Charles A. Sutton
UAI
2003
15 years 3 months ago
On the Convergence of Bound Optimization Algorithms
Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the gener...
Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahra...
ECCV
2008
Springer
16 years 3 months ago
Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition
Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems. Accuracy of inferences in such models depends on the quali...
Cassio Polpo de Campos, Yan Tong, Qiang Ji
ICRA
2009
IEEE
130views Robotics» more  ICRA 2009»
15 years 8 months ago
Model adaptation with least-squares SVM for adaptive hand prosthetics
— The state-of-the-art in control of hand prosthetics is far from optimal. The main control interface is represented by surface electromyography (EMG): the activation potentials ...
Francesco Orabona, Claudio Castellini, Barbara Cap...