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020 _a9783319574219
_9978-3-319-57421-9
024 7 _a10.1007/978-3-319-57421-9
_2doi
050 4 _aQ342
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_2bicssc
072 7 _aTEC009000
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072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aProceedings of ELM-2016
_h[electronic resource] /
_cedited by Jiuwen Cao, Erik Cambria, Amaury Lendasse, Yoan Miche, Chi Man Vong.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 285 p. 143 illus., 126 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aProceedings in Adaptation, Learning and Optimization,
_x2363-6092 ;
_v9
520 _aThis book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning.  Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. .
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aCao, Jiuwen.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_957736
700 1 _aCambria, Erik.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_957737
700 1 _aLendasse, Amaury.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_957738
700 1 _aMiche, Yoan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_957739
700 1 _aVong, Chi Man.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_957740
710 2 _aSpringerLink (Online service)
_957741
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319574202
776 0 8 _iPrinted edition:
_z9783319574226
776 0 8 _iPrinted edition:
_z9783319861579
830 0 _aProceedings in Adaptation, Learning and Optimization,
_x2363-6092 ;
_v9
_957742
856 4 0 _uhttps://doi.org/10.1007/978-3-319-57421-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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