The 1994 IEEE International Conference on Neural Networks
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The 1994 IEEE International Conference on Neural Networks IEEE World Congress on Computational Intelligence, June 27-June 29, 1994, Walt Disney World Dolphin Hotel, Orlando Florida

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Published by Available from IEEE Service Center .
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Subjects:

  • Congresses,
  • Neural computers,
  • Neural networks (Computer science)

Book details:

The Physical Object
FormatUnknown Binding
Number of Pages4777
ID Numbers
Open LibraryOL10998589M
ISBN 100780319036
ISBN 109780780319035

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Abstract: The use of neural networks in financial market prediction presents a major challenge to the design of effective neural network predictors and classifiers. In this paper, the author examines several neural networks to evaluate their capability in prediction and in trend estimation which is treated as a classification :// IEEE Xplore, delivering full text Published in: Proceedings of IEEE International Conference on Neural Networks (ICNN'94) Article #: Date of Conference: 28 June-2 July Date Added to IEEE Xplore: 06 August ISBN Information: Print ISBN: X INSPEC [HaMe94] Hagan, M.T., and M. Menhaj, “Training feed-forward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, Vol. 5, No. 6, , pp. –, This paper reports the first development of the Levenberg-Marquardt algorithm for neural :// Gupta A and Long L Hebbian learning with winner take all for spiking neural networks Proceedings of the international joint conference on Neural Networks, () Gupta A Detecting load conditions in human walking using expectation maximization and neural networks Proceedings of the international joint conference on Neural

  From its institution as the Neural Networks Council in the early s, the IEEE Computational Intelligence Society has rapidly grown into a robust community with a vision for addressing real-world issues with biologically-motivated computational paradigms. The Society offers leading research in nature-inspired problem solving, including neural networks, evolutionary algorithms, fuzzy Abstract: It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: 1) the slow gradient-based learning algorithms are extensively used to train neural networks, and 2) all the parameters of the networks are tuned iteratively by using such Mandie D Complex valued recurrent neural networks for noncircular complex signals Proceedings of the international joint conference on Neural Networks, () Galli L, Loiacono D and Lanzi P Learning a context-aware weapon selection policy for unreal tournament III Proceedings of the 5th international conference on Computational   Abstract. A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in ://

The IEEE International Conference on Neural Networks: IEEE World Congress on Computational Intelligence: June J , Walt Disney World Dolphin Hotel, Orlando, Florida [sponsored by IEEE Neural Networks Council and the IEEE Orlando Section] IEEE Service Center, c set: casebound: set: softbound: set: microfiche v. 1 v. 2 v. 3 v. 4 v. 5 v. 6 v.    IEEE International Conference on Neural Networks November December 1, Perth, Australia GC: Yianni Attikiouzel PCs: Marimuthu Palaniswami, Toshio Fukuda, Robert J. Marks II IEEE International Conference on Neural Networks (part of WCCI) June July 2, , Orlando, Florida, USA GC: Steven K. Brief History of Neural Networks. Donald Hebb reinforced the concept of neurons in his book, (IEEE) first International Conference on Neural Networks drew more than 1, :// Saad, E., Prokhorov, D., Wunsch, D.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks 9(6), – () CrossRef Google Scholar