Advances in Neural Networks – ISNN 2016: 13th International by Long Cheng, Qingshan Liu, Andrey Ronzhin

By Long Cheng, Qingshan Liu, Andrey Ronzhin

This publication constitutes the refereed lawsuits of the thirteenth overseas Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers awarded during this quantity have been conscientiously reviewed and chosen from 104 submissions. The papers disguise many issues of neural network-related study together with sign and picture processing; dynamical behaviors of recurrent neural networks; clever regulate; clustering, type, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.

Show description

Read or Download Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings PDF

Best networks books

Applications of Neural Networks

Purposes of Neural Networks provides an in depth description of thirteen functional functions of neural networks, chosen as the initiatives played via the neural networks are actual and important. The contributions are from best researchers in neural networks and, as a complete, offer a balanced insurance throughout a number of program components and algorithms.

Advances in Bayesian Networks

Lately probabilistic graphical versions, specifically Bayesian networks and determination graphs, have skilled major theoretical improvement inside of parts corresponding to synthetic Intelligence and data. This rigorously edited monograph is a compendium of the latest advances within the zone of probabilistic graphical versions equivalent to selection graphs, studying from info and inference.

Recent Trends in Computer Networks and Distributed Systems Security: Second International Conference, SNDS 2014, Trivandrum, India, March 13-14, 2014, Proceedings

This publication constitutes the refereed court cases of the second one foreign convention on safety in desktop Networks and allotted structures, SNDS 2014, held in Trivandrum, India, in March 2014. The 32 revised complete papers provided including nine brief papers and eight workshop papers have been conscientiously reviewed and chosen from 129 submissions.

Cloud Based 5G Wireless Networks

This SpringerBrief introduces key concepts for 5G instant networks. The authors hide the advance of instant networks that ended in 5G, and the way 5G cellular communique expertise (5G) can now not be outlined by way of a unmarried enterprise version or a standard technical attribute. The mentioned networks capabilities and providers comprise community beginning Virtualization (N-FV), Cloud Radio entry Networks (Cloud-RAN), and cellular Cloud Networking (MCN).

Extra info for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings

Sample text

Comput. Math Appl. 65(10), 1427–1437 (2013) 3. : Feature mining for hyperspectral image classification. Proc. IEEE 101(3), 676–697 (2013) 4. : Discriminative spectral-spatial margin-based semisupervised dimensionality reduction of hyperspectral data. IEEE Geosci. Remote Sens. Lett. 12(2), 224–228 (2015) 5. : Stochastic neighbor projection on manifold for feature extraction. Neurocomputing 74(17), 2780–2789 (2011) Spectral-spatial Classification of Hyperspectral 29 6. : Graph embedding and extensions: a general framework for dimensionality reduction.

IEEE 101(3), 652–675 (2013) 14. : Composite kernels for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006) 15. : Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sens. 52(5), 2666–2677 (2014) 16. : Nonparametric weighted feature extraction for classification. IEEE Trans. Geosci. Remote Sens. 42(5), 1096–1105 (2004) Individual Independent Component Analysis on EEG: Event-Related Responses Vs. cn Abstract.

As a result, such systems usually consist of multiple modules, which could be complex and inefficient. In the meantime, selecting proper features usually requires domain knowledge and can affect both efficiency and performance a lot. Faced with such problems, we start to consider introducing powerful deep learning techniques, such as convolutional neural network (CNN) [11], into the edge detection problem. Unlike traditional shallow learning structures, deep neural networks can learn hierarchical feature representations in their multiplelayer structure.

Download PDF sample

Rated 4.97 of 5 – based on 43 votes