000			03877nam a22006735i 4500
001			978-3-319-11179-7
003			DE-He213
005			20200421112542.0
007			cr nn 008mamaa
008			140818s2014 gw | s |||| 0|eng d
020			_a9783319111797 _9978-3-319-11179-7
024	7		_a10.1007/978-3-319-11179-7 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
245	1	0	_aArtificial Neural Networks and Machine Learning - ICANN 2014 _h[electronic resource] : _b24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings / _cedited by Stefan Wermter, Cornelius Weber, W�odzis�aw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, G�unther Palm, Alessandro E. P. Villa.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aXXV, 852 p. 338 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x0302-9743 ; _v8681
505	0		_aRecurrent Networks -- Sequence Learning -- Echo State Networks -- Recurrent Network Theory -- Competitive Learning and Self-Organisation.- Clustering and Classification -- Trees and Graphs -- Human-Machine Interaction -- Deep Networks.- Theory -- Optimization -- Layered Networks -- Reinforcement Learning and Action -- Vision -- Detection and Recognition -- Invariances and Shape Recovery -- Attention and Pose Estimation -- Supervised Learning -- Ensembles -- Regression -- Classification -- Dynamical Models and Time Series -- Neuroscience -- Cortical Models -- Line Attractors and Neural Fields -- Spiking and Single Cell Models -- Applications -- Users and Social Technologies -- Demonstrations.
520			_aThe book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.
650		0	_aComputer science.
650		0	_aComputers.
650		0	_aAlgorithms.
650		0	_aArtificial intelligence.
650		0	_aImage processing.
650		0	_aPattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputation by Abstract Devices.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aPattern Recognition.
650	2	4	_aInformation Systems Applications (incl. Internet).
650	2	4	_aImage Processing and Computer Vision.
700	1		_aWermter, Stefan. _eeditor.
700	1		_aWeber, Cornelius. _eeditor.
700	1		_aDuch, W�odzis�aw. _eeditor.
700	1		_aHonkela, Timo. _eeditor.
700	1		_aKoprinkova-Hristova, Petia. _eeditor.
700	1		_aMagg, Sven. _eeditor.
700	1		_aPalm, G�unther. _eeditor.
700	1		_aVilla, Alessandro E. P. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319111780
830		0	_aLecture Notes in Computer Science, _x0302-9743 ; _v8681
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-11179-7
912			_aZDB-2-SCS
912			_aZDB-2-LNC
942			_cEBK
999			_c58349 _d58349