Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Proceedings of ELM-2016
~
International Conference on Extreme Learning Machine ((2016 :)
Linked to FindBook
Google Book
Amazon
博客來
Proceedings of ELM-2016
Record Type:
Electronic resources : Monograph/item
Title/Author:
Proceedings of ELM-2016/ edited by Jiuwen Cao ... [et al.].
other author:
Cao, Jiuwen.
corporate name:
International Conference on Extreme Learning Machine
Published:
Cham :Springer International Publishing : : 2018.,
Description:
xiii, 285 p. :ill., digital ;24 cm.
[NT 15003449]:
From the Content: Earthen Archaeological Site Monitoring Data Analysis Using Kernel-based ELM and Non-uniform Sampling TFR -- A Multi-Valued Neuron ELM with Complex-Valued Inputs for System Identification using FRA -- Quaternion Extreme Learning Machine.
Contained By:
Springer eBooks
Subject:
Machine learning - Congresses. -
Online resource:
http://dx.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 ... [et al.]. - Cham :Springer International Publishing :2018. - xiii, 285 p. :ill., digital ;24 cm. - Proceedings in adaptation, learning and optimization,v.92363-6084 ;. - Proceedings in adaptation, learning and optimization ;v.9..
From the Content: Earthen Archaeological Site Monitoring Data Analysis Using Kernel-based ELM and Non-uniform Sampling TFR -- A Multi-Valued Neuron ELM with Complex-Valued Inputs for System Identification using FRA -- Quaternion Extreme Learning Machine.
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:
576368
Machine learning
--Congresses.
LC Class. No.: Q325.5
Dewey Class. No.: 006.31
Proceedings of ELM-2016
LDR
:03020nmm a2200325 a 4500
001
2130460
003
DE-He213
005
20170525133222.0
006
m d
007
cr nn 008maaau
008
181005s2018 gw s 0 eng d
020
$a
9783319574219
$q
(electronic bk.)
020
$a
9783319574202
$q
(paper)
024
7
$a
10.1007/978-3-319-57421-9
$2
doi
035
$a
978-3-319-57421-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.I61 2016
111
2
$a
International Conference on Extreme Learning Machine
$d
(2016 :
$c
Singapore)
$3
3295039
245
1 0
$a
Proceedings of ELM-2016
$h
[electronic resource] /
$c
edited by Jiuwen Cao ... [et al.].
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2018.
300
$a
xiii, 285 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Proceedings in adaptation, learning and optimization,
$x
2363-6084 ;
$v
v.9
505
0
$a
From the Content: Earthen Archaeological Site Monitoring Data Analysis Using Kernel-based ELM and Non-uniform Sampling TFR -- A Multi-Valued Neuron ELM with Complex-Valued Inputs for System Identification using FRA -- Quaternion Extreme Learning Machine.
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
Machine learning
$x
Congresses.
$3
576368
650
1 4
$a
Engineering.
$3
586835
650
2 4
$a
Computational Intelligence.
$3
1001631
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
890894
700
1
$a
Cao, Jiuwen.
$3
2133474
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
830
0
$a
Proceedings in adaptation, learning and optimization ;
$v
v.9.
$3
3295040
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-57421-9
950
$a
Engineering (Springer-11647)
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9339195
電子資源
11.線上閱覽_V
電子書
EB Q325.5
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login