國立虎尾科技大學 |

Proceedings of ELM-2016

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Proceedings of ELM-2016/ edited by Jiuwen Cao, Erik Cambria, Amaury Lendasse, Yoan Miche, Chi Man Vong.
其他作者:	Cao, Jiuwen.
面頁冊數:	XIII, 285 p. 143 illus., 126 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-57421-9
ISBN:	9783319574219

Proceedings of ELM-2016
Proceedings of ELM-2016[electronic resource] /edited by Jiuwen Cao, Erik Cambria, Amaury Lendasse, Yoan Miche, Chi Man Vong. - 1st ed. 2018. - XIII, 285 p. 143 illus., 126 illus. in color.online resource. - Proceedings in Adaptation, Learning and Optimization,92363-6084 ;. - Proceedings in Adaptation, Learning and Optimization,1.

This 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. .

ISBN: 9783319574219

Standard No.: 10.1007/978-3-319-57421-9doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Proceedings of ELM-2016
LDR:03214nam a22003975i 4500 001 998797
003 DE-He213
005 20200704212953.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319574219 $9 978-3-319-57421-9
024 7 $a 10.1007/978-3-319-57421-9 $2 doi
035 $a 978-3-319-57421-9
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Proceedings of ELM-2016 $h [electronic resource] / $c edited by Jiuwen Cao, Erik Cambria, Amaury Lendasse, Yoan Miche, Chi Man Vong.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIII, 285 p. 143 illus., 126 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Proceedings in Adaptation, Learning and Optimization, $x 2363-6084 ; $v 9
520 $a This 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 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Cao, Jiuwen. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1065153
700 1 $a Cambria, Erik. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 883904
700 1 $a Lendasse, Amaury. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1269651
700 1 $a Miche, Yoan. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1290289
700 1 $a Vong, Chi Man. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1290290
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319574202
776 0 8 $i Printed edition: $z 9783319574226
776 0 8 $i Printed edition: $z 9783319861579
830 0 $a Proceedings in Adaptation, Learning and Optimization, $x 2363-6084 ; $v 1 $3 1255773
856 4 0 $u https://doi.org/10.1007/978-3-319-57421-9
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)