By Long Cheng, Qingshan Liu, Andrey Ronzhin
This e-book constitutes the refereed court cases of the thirteenth foreign Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers offered during this quantity have been rigorously reviewed and chosen from 104 submissions. The papers disguise many issues of neural network-related learn together with sign and snapshot processing; dynamical behaviors of recurrent neural networks; clever keep an eye on; clustering, type, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.
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Additional info for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings
As soon as the coincidence numbers are known, the conclusion is that the image matches the class with the largest coincidence number. Then the analyzed pixel is colored by the conventional color for the selected class. I. Vorobiev et al. If the sum is zero, this pixel is treated as unclassiﬁed and colored white. The algorithm of producing the output image after the ﬁrst voting may be represented with the diagram of an artiﬁcial inadaptive neuron (Fig. 1), where T is the number of pixels in the image, Xk is the set of the pixels falling into the pupil focused on the selected pixel, Yi the set of pixels corresponding to the sample number i, ∑ means sum of the brightness coin‐ cidence numbers between Xk and Yi, MAXi is the number of the sample with the greatest number of coincidences in the sets Xk and Yi.
It is an almost perfect classiﬁcation map. 4 Conclusions In this paper, a framework is proposed to improve the classiﬁcation accuracy of hyper spectral image, including spatial information extraction by modiﬁed bilateral ﬁlter, dimensionality reduction by the proposed feature extraction algorithm LPDA, image classiﬁcation by SVMCK. The experiment results have proven that the incorporation of spatial information can signiﬁcantly improve the smoothness of the classiﬁcation map. The experimented NWFE, LFDA, NPE and LPDA all produce better results than that without this preprocessing step.
References 1. : An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge (2005) 2. : Auto-adaptive averaging: detecting artifacts in event-related potential data using a fully automated procedure. Psychophysiology 45(2), 216–228 (2008) 3. : Dimension reduction: additional beneﬁt of an optimal ﬁlter for independent component analysis to extract event-related potentials. J. Neurosci. Methods 201(1), 269–280 (2011) 4. : Applying principal components analysis to event-related potentials: a tutorial.
Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings by Long Cheng, Qingshan Liu, Andrey Ronzhin