-
1
-
-
51249194645
-
A logical calculus of the ideas immanent in nervous activity
-
W. McCulloch and W. Pitts," A logical calculus of the ideas immanent in nervous activity," Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943.
-
(1943)
Bulletin of Mathematical Biophysics
, vol.5
, pp. 115-133
-
-
McCulloch, W.1
Pitts, W.2
-
2
-
-
9244245241
-
Feedforward sigmoidal networks-equicontinuity and fault-tolerance
-
P. Chandra and Y Singh, "Feedforward sigmoidal networks-equicontinuity and fault-tolerance," IEEE Transactions on Neural Networks, vol. 15, no. 6, pp. 1350-1366,2004.
-
(2004)
IEEE Transactions on Neural Networks
, vol.15
, Issue.6
, pp. 1350-1366
-
-
Chandra, P.1
Singh, Y.2
-
3
-
-
0022471098
-
Learning representations by back-propagating errors
-
Oct.
-
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, pp. 533-536, Oct. 1986.
-
(1986)
Nature
, vol.323
, pp. 533-536
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
4
-
-
0000646059
-
Learning internal representations by error propagation
-
Foundations, D. E. Rumelhart, J. L. McClelland, and The PDP Research Group, Eds. Cambridge: MIT Press
-
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Volume I: Foundations, D. E. Rumelhart, J. L. McClelland, and The PDP Research Group, Eds. Cambridge: MIT Press, 1987, pp. 318-362.
-
(1987)
Parallel Distributed Processing
, vol.1
, pp. 318-362
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
5
-
-
0141853652
-
Learning representations by back-propagating errors
-
J. A. Anderson and E. Rosenfeld, Eds. Cambridge, MA, USA: MIT Press
-
-, "Learning representations by back-propagating errors," in Neurocomputing: foundations of research, J. A. Anderson and E. Rosenfeld, Eds. Cambridge, MA, USA: MIT Press, 1988, pp. 696-699.
-
(1988)
Neurocomputing: Foundations of Research
, pp. 696-699
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
6
-
-
84943274699
-
A direct adaptive method for faster backpropagation learning: The RPROP algorithm
-
San Francisco
-
M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: The RPROP algorithm," in Proc. of IEEE conference on Neural Networks, vol. I, San Francisco, 2010, pp. 586-591.
-
(2010)
Proc. of IEEE Conference on Neural Networks
, vol.1
, pp. 586-591
-
-
Riedmiller, M.1
Braun, H.2
-
7
-
-
0028466750
-
Advanced supervised learning in multi-layer perceptrons from backpropagation to adaptive learning algorithms
-
M. Riedmiller, "Advanced supervised learning in multi-layer perceptrons from backpropagation to adaptive learning algorithms," Computer Standards & Interfaces, vol. 16, no. 3, pp. 265-278, 1994.
-
(1994)
Computer Standards & Interfaces
, vol.16
, Issue.3
, pp. 265-278
-
-
Riedmiller, M.1
-
8
-
-
0037238922
-
Empirical evaluation of the improved rprop learning algorithms
-
C. Igel and M. Husken, "Empirical evaluation of the improved rprop learning algorithms," Neurocomputing, vol. 50, no. 0, pp. 105-123, 2003.
-
(2003)
Neurocomputing
, vol.50
, pp. 105-123
-
-
Igel, C.1
Husken, M.2
-
9
-
-
0028543366
-
Training feed forward networks with the Marquardt algorithm
-
M. T. Hagan and M. B. Menhaj, "Training feed forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, pp. 989-993, 1994.
-
(1994)
IEEE Transactions on Neural Networks
, vol.5
, pp. 989-993
-
-
Hagan, M.T.1
Menhaj, M.B.2
-
11
-
-
0001024110
-
First and second order methods for learning: Btetween steepest descent and Newton's method
-
R. Battiti, "First and second order methods for learning: btetween steepest descent and Newton's method," Neural Computation, vol. 4, no. 2, pp. 141-166, 1992.
-
(1992)
Neural Computation
, vol.4
, Issue.2
, pp. 141-166
-
-
Battiti, R.1
-
13
-
-
0024874675
-
Construction of neural networks using the Radon transform
-
S. M. Carroll and B. W. Dickinson, "Construction of neural networks using the Radon transform," in Proc. of the IJCNN, vol. 1, 1989, pp. 607-611.
-
(1989)
Proc. of the IJCNN
, vol.1
, pp. 607-611
-
-
Carroll, S.M.1
Dickinson, B.W.2
-
14
-
-
0003095817
-
Approximation by superposition of a sigmoidal function
-
G. Cybenko, "Approximation by superposition of a sigmoidal function," Mathematics of Control, Signal and Systems, vol. 5, pp. 233-243, 1989.
-
(1989)
Mathematics of Control, Signal and Systems
, vol.5
, pp. 233-243
-
-
Cybenko, G.1
-
15
-
-
0024866495
-
On the approximate realization of continuous mappings by neural networks
-
K. Funahashi, "On the approximate realization of continuous mappings by neural networks," Neural Networks, vol. 2, pp. 183-192, 1989.
-
(1989)
Neural Networks
, vol.2
, pp. 183-192
-
-
Funahashi, K.1
-
16
-
-
0024880831
-
Multilayer feedforward networks are universal approximators
-
K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, pp. 359-366, 1989.
-
(1989)
Neural Networks
, vol.2
, pp. 359-366
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
-
17
-
-
0024933821
-
Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions
-
M. Stinchcombe and H. White, "Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions," in Neural Networks, 1989. IJCNN., International Joint Conference on, 1989, pp. 613-617 vol. I.
-
(1989)
Neural Networks, 1989. IJCNN., International Joint Conference on
, vol.1
, pp. 613-617
-
-
Stinchcombe, M.1
White, H.2
-
18
-
-
0025751820
-
Approximation capabilities of multilayer feed forward networks
-
K. Hornik, "Approximation capabilities of multilayer feed forward networks," Neural Networks, vol. 4, no. 2, pp. 251-257, 1991.
-
(1991)
Neural Networks
, vol.4
, Issue.2
, pp. 251-257
-
-
Hornik, K.1
-
19
-
-
0027262895
-
Multilayer feedforward networks with a non-polynomial activation function can approximate any function
-
M. Leshno, V. Y Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a non-polynomial activation function can approximate any function," Neural Networks, vol. 6, pp. 861-867, 1993.
-
(1993)
Neural Networks
, vol.6
, pp. 861-867
-
-
Leshno, M.1
Lin, V.Y.2
Pinkus, A.3
Schocken, S.4
-
20
-
-
85011438572
-
Approximation theory of the MLP model in neural networks
-
A. Pinkus, "Approximation theory of the MLP model in neural networks," Acta NlImerica, vol. 8, pp. 143-195, 1999.
-
(1999)
Acta NlImerica
, vol.8
, pp. 143-195
-
-
Pinkus, A.1
-
21
-
-
0032643084
-
Multilayer feed forward networks with adaptive spline activation function
-
May
-
S. Guarnieri, F. Piazza, and A. Uncini, "Multilayer feed forward networks with adaptive spline activation function," Nellral Networks, IEEE Transactions on, vol. 10, no. 3, pp. 672-683, May 1999.
-
(1999)
Nellral Networks, IEEE Transactions on
, vol.10
, Issue.3
, pp. 672-683
-
-
Guarnieri, S.1
Piazza, F.2
Uncini, A.3
-
22
-
-
0033699302
-
Artificial neural networks with adaptive multidimensional spline activation functions
-
M. Solazzi and A. Uncini, "Artificial neural networks with adaptive multidimensional spline activation functions," in Nellral Networks, 2000. UCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, vol. 3, 2000, pp. 471-476 vol. 3.
-
(2000)
Nellral Networks, 2000. UCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
, vol.3
, pp. 471-476
-
-
Solazzi, M.1
Uncini, A.2
-
23
-
-
0028428443
-
Regression modeling in back-propagation and projection pursuit learning
-
May
-
J.-N. Hwang, S.-R. Lay, M. Maechler, R. Martin, and J. Schimert, "Regression modeling in back-propagation and projection pursuit learning," Nellral Networks, IEEE Transactions on, vol. 5, no. 3, pp. 342-353, May 1994.
-
(1994)
Nellral Networks, IEEE Transactions on
, vol.5
, Issue.3
, pp. 342-353
-
-
Hwang, J.-N.1
Lay, S.-R.2
Maechler, M.3
Martin, R.4
Schimert, J.5
-
24
-
-
0000400323
-
Survey of neural network transfer functions
-
W. Ouch and N. Jankowski, "Survey of neural network transfer functions," Neural Computing Surveys, vol. 2, pp. 163-212, 1999.
-
(1999)
Neural Computing Surveys
, vol.2
, pp. 163-212
-
-
Ouch, W.1
Jankowski, N.2
-
25
-
-
0842321952
-
Sigmoidal function classes for feedforward artificial neural networks
-
P. Chandra, "Sigmoidal function classes for feedforward artificial neural networks," Nellral Processing Letters, vol. 18, no. 3, pp. 205-215,2003.
-
(2003)
Nellral Processing Letters
, vol.18
, Issue.3
, pp. 205-215
-
-
Chandra, P.1
-
26
-
-
84904803668
-
Bi-modal derivative activation function for sigmoidal feed forward networks
-
S. S. Sodhi and P. Chandra, "Bi-modal derivative activation function for sigmoidal feed forward networks," Nellrocompllting, vol. 143, no. 0, pp. 182-196,2014.
-
(2014)
Nellrocompllting
, vol.143
, pp. 182-196
-
-
Sodhi, S.S.1
Chandra, P.2
-
27
-
-
84872543023
-
Efficient backprop
-
G. B. Orr and K.-R. MUlier, Eds. Berlin: Springer
-
Y. LeCun, L. Bottou, G. B. Orr, and K.-R. Muller, "Efficient backprop," in Nellral Networks: Tricks of the trade, ser. LNCS:1524, G. B. Orr and K.-R. MUlier, Eds. Berlin: Springer, 1998, pp. 9-50.
-
(1998)
Nellral Networks: Tricks of the Trade, Ser. LNCS:1524
, pp. 9-50
-
-
LeCun, Y.1
Bottou, L.2
Orr, G.B.3
Muller, K.-R.4
-
28
-
-
0025839504
-
A Gaussian potential function network with hierarchically self-organizing learning
-
S. Lee and R. M. Kil, "A Gaussian potential function network with hierarchically self-organizing learning," Nellral Networks, vol. 4, no. 2, pp. 207-224, 1991.
-
(1991)
Nellral Networks
, vol.4
, Issue.2
, pp. 207-224
-
-
Lee, S.1
Kil, R.M.2
-
30
-
-
30244511944
-
The PI method for estimating multivariate functions from noisy data
-
L. Breiman, "The PI method for estimating multivariate functions from noisy data," Technometrics, vol. 3, no. 2, pp. 125-160, 1991.
-
(1991)
Technometrics
, vol.3
, Issue.2
, pp. 125-160
-
-
Breiman, L.1
-
31
-
-
0030196823
-
Comparison of adaptive methods for function estimation from samples
-
V. Cherkassky, D. Gehring, and F. MUlier, "Comparison of adaptive methods for function estimation from samples," IEEE Transactions on Nellral Networks, vol. 7, no. 4, pp. 969-984, 1996.
-
(1996)
IEEE Transactions on Nellral Networks
, vol.7
, Issue.4
, pp. 969-984
-
-
Cherkassky, V.1
Gehring, D.2
Mulier, F.3
-
33
-
-
0025541996
-
Projection pursuit learning networks for regression
-
M. Maechler, O. Martin, J. Schimert, M. Csoppenszky, and J. Hwang, "Projection pursuit learning networks for regression," in Proc. of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 1990, pp. 350-358.
-
(1990)
Proc. of the 2nd International IEEE Conference on Tools for Artificial Intelligence
, pp. 350-358
-
-
Maechler, M.1
Martin, O.2
Schimert, J.3
Csoppenszky, M.4
Hwang, J.5
|