Neural Information Processing 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12-15, 2019, Proceedings, Part V /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Gedeon, Tom (Editor, http://id.loc.gov/vocabulary/relators/edt), Wong, Kok Wai (Editor, http://id.loc.gov/vocabulary/relators/edt), Lee, Minho (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Communications in Computer and Information Science, 1143
• Subjects: Pattern recognition.; Optical data processing.; Computer communication systems.; Machine learning.; Computers.; Pattern Recognition.; Image Processing and Computer Vision.; Computer Communication Networks.; Machine Learning.; Information Systems and Communication Service.
• Physical Description: XXIV, 792 p. 304 illus., 230 illus. in color. online resource.
• ISBN: 9783030368029
• ISSN: 1865-0929 ;
• DOI: 10.1007/978-3-030-36802-9

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.