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GECCO
2006
Springer
177views Optimization» more  GECCO 2006»
13 years 8 months ago
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
ESANN
2003
13 years 6 months ago
Approximation of Function by Adaptively Growing Radial Basis Function Neural Networks
In this paper a neural network for approximating function is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose parameters are learn...
Jianyu Li, Siwei Luo, Yingjian Qi
NIPS
2001
13 years 6 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
CORR
2004
Springer
103views Education» more  CORR 2004»
13 years 4 months ago
Online convex optimization in the bandit setting: gradient descent without a gradient
We study a general online convex optimization problem. We have a convex set S and an unknown sequence of cost functions c1, c2, . . . , and in each period, we choose a feasible po...
Abraham Flaxman, Adam Tauman Kalai, H. Brendan McM...
GECCO
2010
Springer
227views Optimization» more  GECCO 2010»
13 years 8 months ago
Benchmarking SPSA on BBOB-2010 noisy function testbed
This paper presents the result for Simultaneous Perturbation Stochastic Approximation (SPSA) on the BBOB 2010 noisy testbed. SPSA is a stochastic gradient approximation strategy w...
Steffen Finck, Hans-Georg Beyer