Electronic Resource

Proceedings of ELM2019

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Penilaian

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This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14–16, 2019. Extreme Learning Machines (ELMs) aim 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 ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.

This conference provides 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.


This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908001261	EB0001867	006.3 Pro	Central Library (OPAC)	Available

Detail Information

Series Title

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Call Number

006.3 Pro

Publisher

Switzerland : Springer Cham., 2020

Collation

VI, 182p.:Ill

Language

English

ISBN/ISSN

978-3-030-58989-9

Classification

006.3

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Artificial intelligence

Specific Detail Info

-

Statement of Responsibility

BRF

Other version/related

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