An introduction to kernel-based learning algorithms
Muller, K.-R.; Mika, S.; Ratsch, G.; Tsuda, K.; Scholkopf, B.
Page(s): 181-201
Digital Object Identifier 10.1109/72.914517 Abstract
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Independent component analysis using Potts models
Jiann-Ming Wu; Shih-Jang Chiu
Page(s): 202-211
Digital Object Identifier 10.1109/72.914518 Abstract
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STRIP - a strip-based neural-network growth algorithm for learning
multiple-valued functions
Ngom, A.; Stojmenovic, I.; Milutinovic, V.
Page(s): 212-227
Digital Object Identifier 10.1109/72.914519 Abstract
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Regularization networks: fast weight calculation via Kalman
filtering
De Nicolao, G.; Ferrari-Trecate, G.
Page(s): 228-235
Digital Object Identifier 10.1109/72.914520 Abstract
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A net with complex weights
Igelnik, B.; Tabib-Azar, M.; LeClair, S.R.
Page(s): 236-249
Digital Object Identifier 10.1109/72.914521 Abstract
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Divide-and-conquer learning and modular perceptron networks
Hsin-Chia Fu; Yen-Po Lee; Cheng-Chin Chiang; Hsiao-Tien Pao
Page(s): 250-263
Digital Object Identifier 10.1109/72.914522 Abstract
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Online learning control by association and reinforcement
Si, J.; Yu-Tsung Wang
Page(s): 264-276
Digital Object Identifier 10.1109/72.914523 Abstract
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A new methodology of extraction, optimization and application of
crisp and fuzzy logical rules
Duch, W.; Adamczak, R.; Grabczewski, K.
Page(s): 277-306
Digital Object Identifier 10.1109/72.914524 Abstract
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Block-based neural networks
Sang-Woo Moon; Seong-Gon Kong
Page(s): 307-317
Digital Object Identifier 10.1109/72.914525 Abstract
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A comparison between HMLP and HRBF for attitude control
Fortuna, L.; Muscato, G.; Xibilia, M.G.
Page(s): 318-328
Digital Object Identifier 10.1109/72.914526 Abstract
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Design and analysis of maximum Hopfield networks
Galan-Marin, G.; Munoz-Perez, J.
Page(s): 329-339
Digital Object Identifier 10.1109/72.914527 Abstract
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The convergence properties of a clipped Hopfield network and its
application in the design of keystream generator
Chi-Kwong Chan; Cheng, L.M.
Page(s): 340-348
Digital Object Identifier 10.1109/72.914528 Abstract
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Global exponential stability of neural networks with globally
Lipschitz continuous activations and its application to linear
variational inequality problem
Xue-Bin Liang; Si, J.
Page(s): 349-359
Digital Object Identifier 10.1109/72.914529 Abstract
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Nonlinear measures: a new approach to exponential stability
analysis for Hopfield-type neural networks
Hong Qiao; Jigen Peng; Zong-Ben Xu
Page(s): 360-370
Digital Object Identifier 10.1109/72.914530 Abstract
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K-winner machines for pattern classification
Ridella, S.; Rovetta, S.; Zunino, R.
Page(s): 371-385
Digital Object Identifier 10.1109/72.914531 Abstract
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Forward- and backpropagation in a silicon dendrite
Rasche, C.; Douglas, R.J.
Page(s): 386-393
Digital Object Identifier 10.1109/72.914532 Abstract
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Texture segmentation using Gaussian-Markov random fields and neural
oscillator networks
Cesmeli, E.; Wang, D.
Page(s): 394-404
Digital Object Identifier 10.1109/72.914533 Abstract
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Self-organizing mixture networks for probability density estimation
Yin, H.; Allinson, N.M.
Page(s): 405-411
Digital Object Identifier 10.1109/72.914534 Abstract
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Some new results on system identification with dynamic neural
networks
Wen Yu; Xiaoou Li
Page(s): 412-417
Digital Object Identifier 10.1109/72.914535 Abstract
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A novel continuous-time neural network for realizing associative
memory
Qing Tao; Tingjian Fang; Hong Qiao
Page(s): 418-423
Digital Object Identifier 10.1109/72.914536 Abstract
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A fast feedforward training algorithm using a modified form of the
standard backpropagation algorithm
Abid, S.; Fnaiech, F.; Najim, M.
Page(s): 424-430
Digital Object Identifier 10.1109/72.914537 Abstract
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Feedforward networks training speed enhancement by optimal
initialization of the synaptic coefficients
Yam, J.Y.F.; Chow, T.W.S.
Page(s): 430-434
Digital Object Identifier 10.1109/72.914538 Abstract
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Nonlinear model structure detection using optimum experimental
design and orthogonal least squares
Hong, X.; Harris, C.J.
Page(s): 435-439
Digital Object Identifier 10.1109/72.914539 Abstract
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Improving the capacity of complex-valued neural networks with a
modified gradient descent learning rule
Errata to "on the use of separable volterra networks to model discrete-time volterra systems"
Adeney, K.A.; Korenberg, M.J.
Page(s): 443-443
Digital Object Identifier 10.1109/TNN.2001.914541 Abstract
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