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» Training Methods for Adaptive Boosting of Neural Networks
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BIBM
2008
IEEE
172views Bioinformatics» more  BIBM 2008»
15 years 4 months ago
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
Protein fold recognition is the prediction of protein’s tertiary structure (Fold) given the protein’s sequence without relying on sequence similarity. Using machine learning t...
Yazhene Krishnaraj, Chandan K. Reddy
EAAI
2006
123views more  EAAI 2006»
14 years 9 months ago
Imitation learning with spiking neural networks and real-world devices
This article is about a new approach in robotic learning systems. It provides a method to use a real-world device that operates in real-time, controlled through a simulated recurr...
Harald Burgsteiner
AI
2001
Springer
15 years 2 months ago
The Bottom-Up Freezing: An Approach to Neural Engineering
This paper presents a new pruning method to determine a nearly optimum multi-layer neural network structure. The aim of the proposed method is to reduce the size of the network by ...
Ali Farzan, Ali A. Ghorbani
ICANN
2010
Springer
14 years 10 months ago
Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors
Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however ...
Dominik Scherer, Hannes Schulz, Sven Behnke
GECCO
2005
Springer
196views Optimization» more  GECCO 2005»
15 years 3 months ago
Breeding swarms: a new approach to recurrent neural network training
This paper shows that a novel hybrid algorithm, Breeding Swarms, performs equal to, or better than, Genetic Algorithms and Particle Swarm Optimizers when training recurrent neural...
Matthew Settles, Paul Nathan, Terence Soule