國立虎尾科技大學 |

Proceedings of ELM-2017

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Proceedings of ELM-2017/ edited by Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse.
其他作者:	Cao, Jiuwen.
面頁冊數:	VII, 340 p. 130 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-01520-6
ISBN:	9783030015206

Proceedings of ELM-2017
Proceedings of ELM-2017[electronic resource] /edited by Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse. - 1st ed. 2019. - VII, 340 p. 130 illus.online resource. - Proceedings in Adaptation, Learning and Optimization,102363-6084 ;. - Proceedings in Adaptation, Learning and Optimization,1.

Adaptive Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine -- Sparse representation feature for facial expression recognition -- Protecting User Privacy in Mobile Environment using ELM-UPP -- Application Study of Extreme Learning Machine in Image Edge Extraction -- A Normalized Mutual Information Estimator Compensating Variance Fluctuations -- Reconstructing Bifurcation Diagrams of Induction Motor Drives using an Extreme Learning Machine -- Ensemble based error minimization reduction forELM -- The Parameter Updating Method Based onKalman Filter for Online Sequential ExtremeLearning Machine -- Extreme Learning Machine BasedShip Detection Using Synthetic Aperture Radar.

This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4–7, 2017. The book covers theories, algorithms and applications of ELM. Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, 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. 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. It gives readers a glance of the most recent advances of ELM. .

ISBN: 9783030015206

Standard No.: 10.1007/978-3-030-01520-6doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Proceedings of ELM-2017
LDR:04025nam a22004095i 4500 001 1004989
003 DE-He213
005 20200705150633.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030015206 $9 978-3-030-01520-6
024 7 $a 10.1007/978-3-030-01520-6 $2 doi
035 $a 978-3-030-01520-6
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-2017 $h [electronic resource] / $c edited by Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a VII, 340 p. 130 illus. $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 10
505 0 $a Adaptive Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine -- Sparse representation feature for facial expression recognition -- Protecting User Privacy in Mobile Environment using ELM-UPP -- Application Study of Extreme Learning Machine in Image Edge Extraction -- A Normalized Mutual Information Estimator Compensating Variance Fluctuations -- Reconstructing Bifurcation Diagrams of Induction Motor Drives using an Extreme Learning Machine -- Ensemble based error minimization reduction forELM -- The Parameter Updating Method Based onKalman Filter for Online Sequential ExtremeLearning Machine -- Extreme Learning Machine BasedShip Detection Using Synthetic Aperture Radar.
520 $a This book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4–7, 2017. The book covers theories, algorithms and applications of ELM. Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, 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. 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. 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 Vong, Chi Man. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1290290
700 1 $a Miche, Yoan. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1290289
700 1 $a Lendasse, Amaury. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1269651
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030015190
776 0 8 $i Printed edition: $z 9783030015213
776 0 8 $i Printed edition: $z 9783030131821
830 0 $a Proceedings in Adaptation, Learning and Optimization, $x 2363-6084 ; $v 1 $3 1255773
856 4 0 $u https://doi.org/10.1007/978-3-030-01520-6
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)