A Neural Network Training Algorithm Based on Collinear Scaling Quasi-Newton Method

2011 ◽  
Author(s):  
Shijie Ye ◽  
Jianliang Li ◽  
Jun Xu
Keyword(s):  
Neural Network ◽  
Newton Method ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Network Training ◽  
Quasi Newton

scholarly journals Machine-learning in astronomy

2014 ◽  
Vol 10 (S306) ◽  
pp. 279-287 ◽  
Author(s):  
Michael Hobson ◽  
Philip Graff ◽  
Farhan Feroz ◽  
Anthony Lasenby
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Gamma Ray ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Data Description ◽  
Astronomical Data ◽  
Machine Learning Methods ◽  
Network Training

AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.

A neural network training algorithm utilizing multiple sets of linear equations

1999 ◽  
Vol 25 (1-3) ◽  
pp. 55-72 ◽  
Author(s):  
Hung-Han Chen ◽  
Michael T. Manry ◽  
Hema Chandrasekaran
Keyword(s):  
Neural Network ◽  
Linear Equations ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Network Training
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