Results 81 to 90 of about 4,010,683 (211)

Evolving Spiking Neural Networks for online learning over drifting data streams [PDF]

, 2018
Publisher Copyright: © 2018 Elsevier LtdNowadays huge volumes of data are produced in the form of fast streams, which are further affected by non-stationary phenomena.
Laña, Ibai, Kasabov, Nikola, Del Ser, Javier, Bilbao, Miren Nekane, Lobo, Jesus L.   +4 more
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Associative Neural Network

Neural Processing Letters, 2002
An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memory of neural networks. The long-term memory is represented by ensemble of neural network weights, while the short-term memory is stored as a pool ...
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Quantum Neural Networks

Journal of Computer and System Sciences, 2001
This paper initiates the study of quantum computing within the constraints of using a polylogarithmic ($O(\log^k n), k\geq 1$) number of qubits and a polylogarithmic number of computation steps. The current research in the literature has focussed on using a polynomial number of qubits. A new mathematical model of computation called \emph{Quantum Neural
Sanjay Gupta, R. K. P. Zia
openaire   +4 more sources

Are Modern Deep Learning Models for Sentiment Analysis Brittleƒ An Examination on Part-of-Speech

, 2020
Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang, Sheng, Q.Z., Abdulwahab Aljubairy, Alhazmi, A., Ahoud Alhazmi, Quan Z Sheng, Zhang, W.E., Aljubairy, A.   +7 more
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Neurophysiological prediction of competitive orientation via EEG during outcome evaluation in gamified learning

npj Science of Learning
Gamification often employs competition-based mechanics to enhance motivation; however, their effectiveness varies depending on an individual’s competitive orientation.
Hiroki Watanabe, Ayato Tabata, Chikara Ishii, Yasushi Naruse   +3 more
doaj   +1 more source

Neural networks in measurement and control

, 1995
A brief overview of neural network is presented. Neural networks have certainly made an impact, and it is now at the stage of getting past the initial hype to see the sort of tasks that neural network are really suited to.
Armitage, Alistair
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Business failure prediction using neural networks and wavelet neural networks

, 2013
Bankruptcy prediction models concerns for decades both academics and practitioners. Moreover, in recent years, during the financial crisis period the development of accurate business failure prediction models is particularly compelling. In this paper, we
Tsinaslanidis, P., Livanis, E., Alexandridis, A., Zapranis, A.   +3 more
core  

Output Partitioning of Neural Networks [PDF]

, 2005
Many constructive learning algorithms have been proposed to find an appropriate network structure for a classification problem automatically. Constructive learning algorithms have drawbacks especially when used for complex tasks and modular approaches ...
Yinan, Q., Li, S., Tan, S.K., Shanchun Li, Qi Yinan, Guan, SU, Syn Kiat Tan, Qi, Y, Tan, SK, Li, S, Guan, S.-U., Sheng-Uei Guan   +11 more
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Compact and low-power wireless headstage for electrocorticography recording of freely moving primates in a home cage

Frontiers in Neuroscience
ObjectiveWireless electrocorticography (ECoG) recording from unrestrained nonhuman primates during behavioral tasks is a potent method for investigating higher-order brain functions over extended periods.
Taro Kaiju, Masato Inoue, Masayuki Hirata, Masayuki Hirata, Takafumi Suzuki   +4 more
doaj   +1 more source

Neural networks for inference, inference for neural networks

, 2018
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. It provides a principled method for representing our prior knowledge, and updating that knowledge in the light of new information. Traditional Bayesian statistics, however, has been limited to simple models.
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