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» Evolving neural network ensembles for control problems
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NN
2006
Springer
114views Neural Networks» more  NN 2006»
13 years 7 months ago
Modular learning models in forecasting natural phenomena
Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input s...
Dimitri P. Solomatine, Michael Baskara L. A. Siek
ICANN
2010
Springer
13 years 8 months ago
Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning
We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuou...
Christian Vollmer, Erik Schaffernicht, Horst-Micha...
NN
2010
Springer
125views Neural Networks» more  NN 2010»
13 years 6 months ago
Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Frank Sehnke, Christian Osendorfer, Thomas Rü...
SCN
2004
Springer
121views Communications» more  SCN 2004»
14 years 1 months ago
ECRYPT: The Cryptographic Research Challenges for the Next Decade
Abstract. In the past thirty years, cryptology has evolved from a secret art to a modern science. Weaker algorithms and algorithms with short keys are disappearing, political contr...
Bart Preneel
APPROX
2006
Springer
89views Algorithms» more  APPROX 2006»
13 years 11 months ago
Online Algorithms to Minimize Resource Reallocations and Network Communication
Abstract. In this paper, we consider two new online optimization problems (each with several variants), present similar online algorithms for both, and show that one reduces to the...
Sashka Davis, Jeff Edmonds, Russell Impagliazzo