عنوان

Proceedings of ELM 2018 /

(Number (ISBN

3030233065

(Number (ISBN

3030233073

(Number (ISBN

3030233081

(Number (ISBN

303023309X

(Number (ISBN

9783030233068

(Number (ISBN

9783030233075

(Number (ISBN

9783030233082

(Number (ISBN

9783030233099

Erroneous ISBN

9783030233068

Title Proper

Proceedings of ELM 2018 /

General Material Designation

[Book]

First Statement of Responsibility

editors, Jiuwen Cao, Chi Man Vong, Yoan Miche and Amaury Lendasse.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2020]

Date of Publication, Distribution, etc.

©2020

Specific Material Designation and Extent of Item

1 online resource.

Series Title

Proceedings in adaptation, learning and optimization ;

Volume Designation

volume 11.

Text of Note

Includes bibliographical references and index.

Text of Note

This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21-23, 2018. This conference provided 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 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. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030233075

International Standard Book Number

3030233065

International Standard Book Number

3030233073

Artificial intelligence, Congresses.

Computational intelligence, Congresses.

Artificial intelligence.

Computational intelligence.

COM004000

UYQ

UYQ

Number

Edition

Class number

Cao, Jiuwen

Lendasse, Amaury

Miche, Yoan

Vong, Chi Man

International Conference on Extreme Learning Machine(2018 :, Singapore)

Date of Transaction

20200823092801.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y