Crossplatform C++ library for multilayer perceptron learning
DOI:
https://doi.org/10.15587/2313-8416.2016.69588Keywords:
artificial neural networks, training, classification, backpropagation, multilayer perceptronAbstract
Cross-platform C++ library is developed. It provides classes enabling to create multilayer perceptron and its training by supervised learning method (backpropagation). Resulting artificial network is able to classify incoming data after previous training. Multilayer perceptron training results and its ability to classify test dataset are tested
References
Duda, R. O., Hart, P. E., Stork, D. G. (2000). Pattern Classification. New York: Wiley, 272–286.
Haykin, S. O. (2009). Neural Networks and Learning Machines. London; UK: Pearson, 797–798.
Karsoliya, S. (2012). Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture. International Journal of Engineering Trends and Technology, 3 (6), 714–717.
Swain, M. (2012). An Approach for IRIS Plant Classification Using Neural Network. International Journal on Soft Computing, 3 (1), 79–89. doi: 10.5121/ijsc.2012.3107
Linear Classification with SLP. Available at: https://grey.colorado.edu/emergent/index.php/Linear_Classification_with_SLP
Shanker, M. S. (1996). Using Neural Networks To Predict the Onset of Diabetes Mellitus. Journal of Chemical Information and Computer Sciences, 36 (1), 35–41. doi: 10.1021/ci950063e
Using C4.5 to predict Diabetes in Pima Indian Women. Available at: https://datayo.wordpress.com/2015/05/13/using-c4-5/
An incomplete list of C++ compilers. Available at: http://www.stroustrup.com/compilers.html
Neuroph: Smart Java Apps with Neural Networks. Available at: https://dzone.com/articles/understanding-garbage-collection-log
The Java® Virtual Machine Specification. Available at: http://docs.oracle.com/javase/specs/jvms/se7/html/index.html
Documentation. Available at: http://www.cs.waikato.ac.nz/ml/weka/documentation.html
MATLAB Jazyk tehnicheskih vychislenij. Available at: http://www.exponenta.ru/educat/free/matlab/gs.pdf
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Copyright (c) 2016 Дмитрий Тариельевич Ибадов, Ирина Витальевна Афанасьева
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