عنوان

Proceedings of ELM 2018 /

شابک

3030233065

شابک

3030233073

شابک

3030233081

شابک

303023309X

شابک

9783030233068

شابک

9783030233075

شابک

9783030233082

شابک

9783030233099

شابک اشتباه

9783030233068

عنوان اصلي

Proceedings of ELM 2018 /

نام عام مواد

[Book]

نام نخستين پديدآور

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

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2020]

تاریخ نشرو بخش و غیره

©2020

نام خاص و کميت اثر

1 online resource.

عنوان فروست

Proceedings in adaptation, learning and optimization ;

مشخصه جلد

volume 11.

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030233075

شماره استاندارد بين المللي کتاب و موسيقي

3030233065

شماره استاندارد بين المللي کتاب و موسيقي

3030233073

موضوع مستند نشده

Artificial intelligence, Congresses.

موضوع مستند نشده

Computational intelligence, Congresses.

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Computational intelligence.

موضوع مستند نشده

COM004000

موضوع مستند نشده

UYQ

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Cao, Jiuwen

مستند نام اشخاص تاييد نشده

Lendasse, Amaury

مستند نام اشخاص تاييد نشده

Miche, Yoan

مستند نام اشخاص تاييد نشده

Vong, Chi Man

مستند نام تنالگان تاييد نشده

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

تاريخ عمليات

20200823092801.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y