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IJCNN
2008
IEEE
15 years 10 months ago
Evolving a neural network using dyadic connections
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
Andreas Huemer, Mario A. Góngora, David A. ...
BMCBI
2008
153views more  BMCBI 2008»
15 years 4 months ago
Improved general regression network for protein domain boundary prediction
Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary ...
Paul D. Yoo, Abdur R. Sikder, Bing Bing Zhou, Albe...
CEC
2009
IEEE
15 years 11 months ago
Lamarckian neuroevolution for visual control in the Quake II environment
Abstract— A combination of backpropagation and neuroevolution is used to train a neural network visual controller for agents in the Quake II environment. The agents must learn to...
Matt Parker, Bobby D. Bryant
FLAIRS
2000
15 years 5 months ago
Systematic Treatment of Failures Using Multilayer Perceptrons
This paper discusses the empirical evaluation of improving generalization performance of neural networks by systematic treatment of training and test failures. As a result of syst...
Fadzilah Siraj, Derek Partridge
AR
2007
105views more  AR 2007»
15 years 4 months ago
Reinforcement learning of a continuous motor sequence with hidden states
—Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There a...
Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sug...