|
Showing 1 - 16 of
16 matches in All Departments
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.
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.
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.
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.
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.
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.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2015, which was held in
Hangzhou, China, December 15-17, 2015. This conference brought
together researchers and engineers to share and exchange R&D
experience on both theoretical studies and practical applications
of the Extreme Learning Machine (ELM) technique and brain learning.
This book covers theories, algorithms ad applications of ELM. It
gives readers a glance of the most recent advances of ELM.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2015, which was held in
Hangzhou, China, December 15-17, 2015. This conference brought
together researchers and engineers to share and exchange R&D
experience on both theoretical studies and practical applications
of the Extreme Learning Machine (ELM) technique and brain learning.
This book covers theories, algorithms ad applications of ELM. It
gives readers a glance of the most recent advances of ELM.
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.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2014, which was held in
Singapore, December 8-10, 2014. This conference brought together
the researchers and practitioners of Extreme Learning Machine (ELM)
from a variety of fields to promote research and development of
"learning without iterative tuning". The book covers theories,
algorithms and applications of ELM. It gives the readers a glance
of the most recent advances of ELM.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2014, which was held in
Singapore, December 8-10, 2014. This conference brought together
the researchers and practitioners of Extreme Learning Machine (ELM)
from a variety of fields to promote research and development of
"learning without iterative tuning". The book covers theories,
algorithms and applications of ELM. It gives the readers a glance
of the most recent advances of ELM.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2015, which was held in
Hangzhou, China, December 15-17, 2015. This conference brought
together researchers and engineers to share and exchange R&D
experience on both theoretical studies and practical applications
of the Extreme Learning Machine (ELM) technique and brain learning.
This book covers theories, algorithms ad applications of ELM. It
gives readers a glance of the most recent advances of ELM.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2015, which was held in
Hangzhou, China, December 15-17, 2015. This conference brought
together researchers and engineers to share and exchange R&D
experience on both theoretical studies and practical applications
of the Extreme Learning Machine (ELM) technique and brain learning.
This book covers theories, algorithms ad applications of ELM. It
gives readers a glance of the most recent advances of ELM.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2014, which was held in
Singapore, December 8-10, 2014. This conference brought together
the researchers and practitioners of Extreme Learning Machine (ELM)
from a variety of fields to promote research and development of
"learning without iterative tuning". The book covers theories,
algorithms and applications of ELM. It gives the readers a glance
of the most recent advances of ELM.
This book contains some selected papers from the International
Conference on Extreme Learning Machine 2014, which was held in
Singapore, December 8-10, 2014. This conference brought together
the researchers and practitioners of Extreme Learning Machine (ELM)
from a variety of fields to promote research and development of
"learning without iterative tuning". The book covers theories,
algorithms and applications of ELM. It gives the readers a glance
of the most recent advances of ELM.
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.
|
|