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Neural Information Processing 26th International Conference Iconip 2019 Sydney Nsw Australia December 1215 2019 Proceedings Part V 1st Ed 2019 Tom Gedeon

  • SKU: BELL-10801754
Neural Information Processing 26th International Conference Iconip 2019 Sydney Nsw Australia December 1215 2019 Proceedings Part V 1st Ed 2019 Tom Gedeon
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Neural Information Processing 26th International Conference Iconip 2019 Sydney Nsw Australia December 1215 2019 Proceedings Part V 1st Ed 2019 Tom Gedeon instant download after payment.

Publisher: Springer International Publishing
File Extension: PDF
File size: 60.79 MB
Author: Tom Gedeon, Kok Wai Wong, Minho Lee
ISBN: 9783030368012, 9783030368029, 3030368017, 3030368025
Language: English
Year: 2019
Edition: 1st ed. 2019

Product desciption

Neural Information Processing 26th International Conference Iconip 2019 Sydney Nsw Australia December 1215 2019 Proceedings Part V 1st Ed 2019 Tom Gedeon by Tom Gedeon, Kok Wai Wong, Minho Lee 9783030368012, 9783030368029, 3030368017, 3030368025 instant download after payment.

The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019.

For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

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