Advances in Systems Science: Proceedings of the by Jerzy Świątek, Jakub M. Tomczak

By Jerzy Świątek, Jakub M. Tomczak

This e-book gathers the rigorously reviewed court cases of the nineteenth overseas convention on structures technological know-how, providing fresh learn findings within the components of synthetic Intelligence, computer studying, Communication/Networking and knowledge know-how, keep watch over conception, choice help, picture Processing and desktop imaginative and prescient, Optimization options, development acceptance, Robotics, carrier technological know-how, Web-based providers, doubtful platforms and Transportation structures. The foreign convention on structures technological know-how was once held in Wroclaw, Poland from September 7 to nine, 2016, and addressed a variety of subject matters, together with platforms conception, regulate thought, computing device studying, synthetic intelligence, sign processing, conversation and data applied sciences, transportation structures, multi-robotic structures and unsure structures, in addition to their functions. the purpose of the convention is to supply a platform for verbal exchange among younger and tested researchers and practitioners, fostering destiny joint study in platforms science.

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On some properties of the low-dimensional Gumbel perturbations in the Perturb-and-MAP model. Stat. Probab. Lett. 115, 8–15 (2016). 019 23. : Learning Invariant Features Using Subspace Restricted Boltzmann Machine. Neural Process. Lett. 1–10 (2016). 1007/ s11063-016-9519-9 24. : Learning categories from few examples with multi model knowledge transfer. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 928–941 (2014) 25. : A new benchmark dataset for handwritten character recognition, pp. 2–5. Tilburg University (2009) 26.

We have optimized following parameters: decrease/increase factor for model weights, prediction acceptance threshold and parameter representing initial ensemble size. In Fig. 2 the x axis represents the tested methods and the y axis shows the mean absolute percentage error of prediction. Except the time reduction, the optimization brought also a moderate decrease of prediction error. The results indicate that the prediction error of the optimized version was reduced in some cases by more than one percent in comparison to original method.

Removed models were then replaced by new models that were chosen by proposed heuristic. e. iteration where the last models have been removed. The main idea of this heuristic is to add models with better results in comparison with the results of removed models. So, if the number of better models is lower than number of removed models, it is not necessary to preserve the original number of models. Pruning will be applied again when the size of ensemble reaches the maximum number of models. During implementation and initial testing of proposed method and predictive models, we used recommended configuration parameters, described in the studied papers [10, 11, 15, 19].

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