Titlebook: Engineering Applications of Neural Networks; 16th International C Lazaros Iliadis,Chrisina Jayne Conference proceedings 2015 Springer Inter

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https://doi.org/10.1007/978-1-349-09001-3n locally optimized parameters and globally optimized parameters. The multithreaded local learning regularization neural networks are implemented with OpenMP. The accuracy of the algorithms is tested against several benchmark datasets. The parallel efficiency and speedup is evaluated on a multi-core system.

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On-line Surface Roughness Prediction in Grinding Using Recurrent Neural Networks in this work the prediction of the surface roughness (..) evolution based on Recurrent Neural Networks is presented with the capability to generalize to new grinding wheels and conditions. Results show excellent prediction of the surface finish evolution. The absolute maximum error is below 0.49μm, being the average error around 0.32μm.

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Multithreaded Local Learning Regularization Neural Networks for Regression Tasksn locally optimized parameters and globally optimized parameters. The multithreaded local learning regularization neural networks are implemented with OpenMP. The accuracy of the algorithms is tested against several benchmark datasets. The parallel efficiency and speedup is evaluated on a multi-core system.

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Self-Train LogitBoost for Semi-supervised Learninghe?Logitboost regression tree model?is more confident?at the unlabeled instances.?We performed a comparison with other well-known semi-supervised classification methods on standard benchmark datasets and the presented technique had better accuracy in most cases.

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Conference proceedings 2015ormatics; intelligent medical modeling; life-earth sciences intelligent modeling; learning-algorithms; intelligent telecommunications modeling; fuzzy modeling; robotics and control; smart cameras; pattern recognition-facial mapping; classification; financial intelligent modeling; echo state networks..

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