V. V. Romanuke


A 13-itemed scenario framework for classifier single training parameter optimization is developed. Formally, the problem is to find global extremum (mostly, minimum) of function as a classifier output parameter against its single training parameter. Linking the scenario theory to praxis, the classifier type has been decided on two-layer perceptron. Its input objects are monochrome images of a medium format, having a few thousands independent features. Within the framework, the programming environment has been decided on MATLAB, having powerful Neural Network Toolbox. Keeping in mind the stochasticity of the being minimized function, there is defined statistical ε-stability of its evaluation by a finite set of data. These data are mined in batch testings of the trained classifier. For exemplification of the scenario framework, there is optimized pixel-to-turn standard deviations ratio for training two-layer perceptron in classifying monochrome 60-by-80-images of the enlarged 26 English alphabet capital letters. The goal is to find a pixel-to-turn standard deviations ratio for the training process in order to ensure minimum of classification error percentage. The optimization relative gain is about a third. The developed framework can be applied also for classifier multivariable optimization, wherein it instructs which item operations shall regard the corresponding multiplicity of variables.


classifier training parameter optimization, statistical evaluation, optimization scenario, two-layer perceptron, classification error percentage, turned objects classification, monochrome image, pixel-to-turn standard deviations ratio, training set.


Axinte D. A. Approach into the use of probabilistic neural networks for automated classification of tool malfunctions in broaching / D. A. Axinte // International Journal of Machine Tools and Manufacture. – 2006. – Volume 46, Issue 12–13. – P. 1445–1448. DOI: 10.1016/j.ijmachtools.2005.09.017 2. Fukushima K. Increasing robustness against background noise: Visual pattern recognition by a neocognitron / K. Fukushima // Neural Networks. – 2011. – Volume 24, Issue 7. – P. 767–778. DOI: 10.1016/j.neunet.2011.03.017 3. On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images / [J. Plaza, A. Plaza, R. Perez, P. Martinez] // Pattern Recognition. – 2009. – Volume 42, Issue 11. – P. 3032–3045. DOI: 10.1016/j.patcog.2009.04.008 4. Siniscalchi S. M. Exploiting deep neural networks for detection-based speech recognition / S. M. Siniscalchi, D. Yu, L. Deng, C.-H. Lee // Neurocomputing. – 2013. – Volume 106. – P. 148 – 157. DOI: 10.1016/j.neucom.2012.11.008 5. Arulampalam G. A generalized feedforward neural network architecture for classification and regression / G. Arulampalam, A. Bouzerdoum // Neural Networks. – 2003. – Volume 16, Issue 5–6. – P. 561 – 568. DOI: 10.1016/S0893-6080(03)00116-3 6. Multi-column deep neural network for traffic sign classification / [D. Cireşan, U. Meier, J. Masci, J. Schmidhuber] // Neural Networks. – 2012. – Volume 32. – P. 333–338. DOI: 10.1016/j.neunet.2012.02.023 7. Fukushima K. Artificial vision by multi-layered neural networks: Neocognitron and its advances / K. Fukushima // Neural Networks. – 2013. – Volume 37. – P. 103–119. DOI: 10.1016/j.neunet.2012.09.016 8. An efficient hidden layer training method for the multilayer perceptron / [C. Yu, M. T. Manry, J. Li, P. L. Narasimha] // Neurocomputing. – 2006. – Volume 70, Issue 1–3. – P. 525– 535. DOI: 10.1016/j.neucom.2005.11.008 9. Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters / [P. A. Castillo, J. J. Merelo, M. G. Arenas, G. Romero] // Information Sciences. – 2007. – Volume 177, Issue 14. – P. 2884–2905. DOI: 10.1016/j.ins.2007.02.021 10. Hoi K. I. Improvement of the multilayer perceptron for air quality modelling through an adaptive learning scheme / K. I. Hoi, K. V. Yuen, K. M. Mok // Computers & Geosciences. – 2013. – Volume 59. – P. 148–155. DOI: 10.1016/j.cageo.2013.06.002

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