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» On the Complexity of Function Learning
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ML
2000
ACM
244views Machine Learning» more  ML 2000»
15 years 7 days ago
Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning
A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombinat...
Ryszard S. Michalski
100
Voted
SAGT
2010
Springer
175views Game Theory» more  SAGT 2010»
14 years 11 months ago
On Learning Algorithms for Nash Equilibria
Can learning algorithms find a Nash equilibrium? This is a natural question for several reasons. Learning algorithms resemble the behavior of players in many naturally arising gam...
Constantinos Daskalakis, Rafael Frongillo, Christo...
106
Voted
EUROGP
2009
Springer
132views Optimization» more  EUROGP 2009»
15 years 7 months ago
A Statistical Learning Perspective of Genetic Programming
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in GP from the perspec...
Nur Merve Amil, Nicolas Bredeche, Christian Gagn&e...
90
Voted
GECCO
2006
Springer
153views Optimization» more  GECCO 2006»
15 years 4 months ago
Analysis of the difficulty of learning goal-scoring behaviour for robot soccer
Learning goal-scoring behaviour from scratch for simulated robot soccer is considered to be a very difficult problem, and is often achieved by endowing players with an innate set ...
Jeff Riley, Victor Ciesielski
106
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JMLR
2007
137views more  JMLR 2007»
15 years 10 days ago
Building Blocks for Variational Bayesian Learning of Latent Variable Models
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, a...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka...