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NIPS
2007
15 years 8 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
DASFAA
2009
IEEE
104views Database» more  DASFAA 2009»
16 years 1 months ago
On the Discovery of Conserved XML Query Patterns for Evolution-Conscious Caching
Existing xml query pattern-based caching strategies focus on extracting the set of frequently issued query pattern trees (qpt) based on the support of the qpts in the history. Thes...
Sourav S. Bhowmick
ATAL
2009
Springer
16 years 1 months ago
State-coupled replicator dynamics
This paper introduces a new model, i.e. state-coupled replicator dynamics, expanding the link between evolutionary game theory and multiagent reinforcement learning to multistate ...
Daniel Hennes, Karl Tuyls, Matthias Rauterberg
GECCO
2007
Springer
129views Optimization» more  GECCO 2007»
16 years 1 months ago
Achieving a simple development model for 3D shapes: are chemicals necessary?
Artificial Development Systems have been introduced as a technique aimed at increasing the scalability of evolutionary algorithms. Most commonly the development model is part of ...
Pauline C. Haddow, Johan Hoye
ICANNGA
2007
Springer
153views Algorithms» more  ICANNGA 2007»
16 years 1 months ago
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (G...
Daniel Rivero, Juan R. Rabuñal, Julian Dora...