-
2
-
-
0001024110
-
First- and second-order methods for learning: Between steepest descent and Newton's method
-
R. Battiti, “First- and second-order methods for learning: Between steepest descent and Newton's method,” Neural Computation, vol. 4, pp. 141–166, 1992.
-
(1992)
Neural Computation
, vol.4
, pp. 141-166
-
-
Battiti, R.1
-
4
-
-
84950643380
-
Intervention analysis with applications to economic and environmental problems
-
G. E. Box and G. C. Tiao, “Intervention analysis with applications to economic and environmental problems,” J. Amer. Stat. Assoc., vol. 70, 1975.
-
(1975)
J. Amer. Stat. Assoc.
, vol.70
-
-
Box, G.E.1
Tiao, G.C.2
-
5
-
-
0003802343
-
-
Belmont, CA: Wadsworth International Group
-
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth International Group, 1984.
-
(1984)
Classification and Regression Trees
-
-
Breiman, L.1
Friedman, J.H.2
Olshen, R.A.3
Stone, C.J.4
-
6
-
-
0001590347
-
The identification of polynomial systems by means of higher order spectra
-
D. R. Brillinger, “The identification of polynomial systems by means of higher order spectra,” J. Sound Vib., vol. 12, pp. 301–313, 1970.
-
(1970)
J. Sound Vib.
, vol.12
, pp. 301-313
-
-
Brillinger, D.R.1
-
7
-
-
85083614188
-
Time series and neural network modeling
-
doctoral dissertation, Univ. of Washington
-
J. T. Connor, “Time series and neural network modeling,” doctoral dissertation, Univ. of Washington, 1993.
-
(1993)
-
-
Connor, J.T.1
-
8
-
-
0344111838
-
Recurrent and NARMA modeling
-
J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds. San Matteo, CA: Morgan Kauffman
-
J. T. Connor, L. E. Atlas, and R. D. Martin, “Recurrent and NARMA modeling,” Advances in Neural Information Processing Systems 4, J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds. San Matteo, CA: Morgan Kauffman, pp. 301–308, 1992.
-
(1992)
Advances in Neural Information Processing Systems, 4
, pp. 301-308
-
-
Connor, J.T.1
Atlas, L.E.2
Martin, R.D.3
-
9
-
-
0002629270
-
Maximum likelihood from incomplete data via the EM algorithm
-
A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, B, vol. 39, pp. 1–38, 1977.
-
(1977)
Journal of the Royal Statistical Society, B
, vol.39
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
10
-
-
26444565569
-
Finding structure in time
-
J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, pp. 179–21, 1990.
-
(1990)
Cognitive Science
, vol.14
, pp. 179-221
-
-
Elman, J.L.1
-
11
-
-
0002432565
-
Multivariate adaptive regression splines
-
J. H. Friedman, “Multivariate adaptive regression splines,” The Annals of Statistics, vol. 19, pp. 1–141, 1991.
-
(1991)
The Annals of Statistics
, vol.19
, pp. 1-141
-
-
Friedman, J.H.1
-
13
-
-
0017120827
-
Adaptive pattern classification and universal recoding: 1. Parallel development and coding of neural feature detectors
-
S. Grossberg, “Adaptive pattern classification and universal recoding: 1. Parallel development and coding of neural feature detectors,” Biol. Cybern., vol. 23, pp. 121–134, 1976.
-
(1976)
Biol. Cybern.
, vol.23
, pp. 121-134
-
-
Grossberg, S.1
-
15
-
-
0000999440
-
Learning and relearning in Boltzman machines
-
D. E. Rumelhart, J. L. McClelland, Cambridge, MA: MIT Press, Chapter 7
-
G. E. Hinton, and T. J. Sejnowski, “Learning and relearning in Boltzman machines,” D. E. Rumelhart, J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations, Cambridge, MA: MIT Press, Chapter 7, 1986.
-
(1986)
Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations
, vol.1
-
-
Hinton, G.E.1
Sejnowski, T.J.2
-
16
-
-
0020118274
-
Neural networks and physical systems with emergent collective computational abilities
-
J. J. Hopfield “Neural networks and physical systems with emergent collective computational abilities,” Proc. Natl. Acad. Sci. USA, vol. 79, pp. 2554–2558, 1982.
-
(1982)
Proc. Natl. Acad. Sci. USA
, vol.79
, pp. 2554-2558
-
-
Hopfield, J.J.1
-
19
-
-
0038477185
-
Robust estimation of power spectra
-
B. Kleiner, R. D. Martin, and D. J. Thomson, “Robust estimation of power spectra,” J. Roy. Stat. Soc. Ser. B vol 41, pp. 313–351, 1979.
-
(1979)
J. Roy. Stat. Soc. Ser. B
, vol.41
, pp. 313-351
-
-
Kleiner, B.1
Martin, R.D.2
Thomson, D.J.3
-
21
-
-
0003645482
-
Nonlinear signal processing using neural networks: Prediction and Modeling
-
A. Lapedes, and R. Farber, “Nonlinear signal processing using neural networks: Prediction and Modeling,” Technical Report, LA-UR87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico, 1987.
-
(1987)
Technical Report, LA-UR87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico
-
-
Lapedes, A.1
Farber, R.2
-
22
-
-
0011216908
-
Bayesian non-linear modeling for the energy prediction completion
-
Draft 1.3.
-
D. J. C. MacKay, “Bayesian non-linear modeling for the energy prediction completion,” Draft 1.3., 1993.
-
(1993)
-
-
MacKay, D.J.C.1
-
23
-
-
85083609846
-
Approximate conditional mean non-Gaussian filters
-
Dept. of Statistics, Univ. of Washington
-
R. D. Martin, and R. Fraiman, “Approximate conditional mean non-Gaussian filters,” Tech. Report, Dept. of Statistics, Univ. of Washington, 1993.
-
(1993)
Tech. Report
-
-
Martin, R.D.1
Fraiman, R.2
-
24
-
-
0002862560
-
Robust estimation of autoregressive models (with discussion)
-
D. R. Brillinger and G. C. Tiao, Eds. Institute of Mathematical Statistics, Hayward, Calif.
-
R. D. Martin, “Robust estimation of autoregressive models (with discussion),” in Directions in Time Series, D. R. Brillinger and G. C. Tiao, Eds. Institute of Mathematical Statistics, Hayward, Calif., 1980, pp. 228–262.
-
(1980)
Directions in Time Series
, pp. 228-262
-
-
Martin, R.D.1
-
25
-
-
0020183045
-
Robust-resistant spectrum estimation
-
R. D. Martin, and D. J. Thompson, “Robust-resistant spectrum estimation,” Proc. IEEE, vol. 70, pp. 1097–1115, 1982.
-
(1982)
Proc. IEEE
, vol.70
, pp. 1097-1115
-
-
Martin, R.D.1
Thompson, D.J.2
-
26
-
-
85083601481
-
Highly robust estimation of autoregression integrated time series models
-
R. D. Martin and V. J. Yohai, “Highly robust estimation of autoregression integrated time series models,” Tech. Report., 1985.
-
(1985)
Tech. Report
-
-
Martin, R.D.1
Yohai, V.J.2
-
27
-
-
0037877489
-
Robust estimation for time series autoregressions
-
R. L. Launer and G. N. Wilkinson, Eds. New York: Academic Press
-
R. D. Martin, “Robust estimation for time series autoregressions,” in Robustness in Statistics, R. L. Launer and G. N. Wilkinson, Eds. New York: Academic Press, pp. 147–176, 1979.
-
(1979)
Robustness in Statistics
, pp. 147-176
-
-
Martin, R.D.1
-
28
-
-
0016473357
-
Approximate non-Gaussian filtering with linear state and observation relations
-
C. J. Masreliez, “Approximate non-Gaussian filtering with linear state and observation relations,” IEEE Transactions on Automatic Control. pp. 107–110, 1975.
-
(1975)
IEEE Transactions on Automatic Control
, pp. 107-110
-
-
Masreliez, C.J.1
-
30
-
-
0002291616
-
Neural net architectures for temporal sequence processing
-
A. Weigend and N. Gershenfeld, Eds. Redwood City, CA: Addison-Wesley
-
M. C. Mozer, “Neural net architectures for temporal sequence processing,” in Predicting the Future and Understanding the Past, A. Weigend and N. Gershenfeld, Eds. Redwood City, CA: Addison-Wesley.
-
Predicting the Future and Understanding the Past
-
-
Mozer, M.C.1
-
31
-
-
0003541323
-
Soft competitive adaptation: Neural network learning algorithm based on fitting statistical mixtures
-
Tech. Report, CMU-CS-91-126, CMU, Pittsburgh
-
S. J. Nowlan, “Soft competitive adaptation: Neural network learning algorithm based on fitting statistical mixtures,” Tech. Report, CMU-CS-91-126, CMU, Pittsburgh.
-
-
-
Nowlan, S.J.1
-
32
-
-
0026157963
-
An adaptively trained neural network
-
D. C. Park, M. A. El-Sharkawi, and R. J. Marks, “An adaptively trained neural network,” IEEE Trans. on Neural Networks. vol. 2, no. 3, 1991.
-
(1991)
IEEE Trans. on Neural Networks
, vol.2
, Issue.3
-
-
Park, D.C.1
El-Sharkawi, M.A.2
Marks, R.J.3
-
33
-
-
0023534496
-
Generalization of backpropagation to recurrent and higher order networks
-
F. J. Pineda, “Generalization of backpropagation to recurrent and higher order networks,” in Proc. IEEE Conf. Neural Inform. Proc. Syst. 1987.
-
(1987)
Proc. IEEE Conf. Neural Inform. Proc. Syst.
-
-
Pineda, F.J.1
-
34
-
-
0003838146
-
The utility driven dynamic error propagation network
-
Tech Report CUED/F-1NFENG/TR.L Cambridge: Cambridge University, Department of Engineering
-
A. J. Robinson and F. Fallside, “The utility driven dynamic error propagation network,” Tech Report CUED/F-1NFENG/TR.L Cambridge: Cambridge University, Department of Engineering.
-
-
-
Robinson, A.J.1
Fallside, F.2
-
35
-
-
0003444646
-
Learning internal representations by error propagation
-
D. E. Rumelhart, and J. L. McCelland, Eds. Cambridge, MA: MIT Press
-
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing, vol. 1, D. E. Rumelhart, and J. L. McCelland, Eds. Cambridge, MA: MIT Press, pp. 318–362, 1986.
-
(1986)
Parallel Distributed Processing
, vol.1
, pp. 318-362
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
36
-
-
0000053463
-
A fixed size storage O(n3) time complexity learning algorithm for fully recurrent continually running networks
-
J. Schmidhuber, “A fixed size storage O(n3) time complexity learning algorithm for fully recurrent continually running networks,” Neural Computation, vol. 4, pp. 243–248, 1992.
-
(1992)
Neural Computation
, vol.4
, pp. 243-248
-
-
Schmidhuber, J.1
-
37
-
-
0000366506
-
On the theory of bilinear models
-
T. Subba Rao, “On the theory of bilinear models,”.1. Roy. Statist. Soc. Ser. B, vol. 43, pp. 244–255, 1981.
-
(1981)
J. Roy. Statist. Soc. Ser. B
, vol.43
, pp. 244-255
-
-
Subba Rao, T.1
-
39
-
-
0024634603
-
Phoneme recognition using time delay neural networks
-
A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. J. Lang, “Phoneme recognition using time delay neural networks,” IEEE Trans. on Acous. Speech and Sig. Proc., vol. 37, pp. 324–339, 1989.
-
(1989)
IEEE Trans. on Acous. Speech and Sig. Proc.
, vol.37
, pp. 324-339
-
-
Waibel, A.1
Hanazawa, T.2
Hinton, G.3
Shikano, K.4
Lang, K.J.5
-
40
-
-
0025503558
-
Backpropagation through time: What it does and how to do it
-
Oct.
-
P. J. Werbos, “Backpropagation through time: What it does and how to do it,” Proc. of the IEEE, pp. 1550–1560, vol. 78, no. 10, Oct. 1990.
-
(1990)
Proc. of the IEEE
, vol.78
, Issue.10
, pp. 1550-1560
-
-
Werbos, P.J.1
-
41
-
-
0001202594
-
A teaming algorithm for continually running fully recurrent neural networks
-
R. Williams and D. Zipser, “A teaming algorithm for continually running fully recurrent neural networks,” Neural Computation, vol. 1, pp. 270–280, 1989.
-
(1989)
Neural Computation
, vol.1
, pp. 270-280
-
-
Williams, R.1
Zipser, D.2
-
42
-
-
0000729019
-
Forecasting demand for electric power
-
S. J. Hanson, J. D. Cowan, and C. L. Giles, Eds. San Matteo, CA: Morgan Kauffman
-
Jen-Lun Yuan and T. L. Fine, “Forecasting demand for electric power,” Advances in Neural Information Processing Systems vol. 5. S. J. Hanson, J. D. Cowan, and C. L. Giles, Eds. San Matteo, CA: Morgan Kauffman, pp. 739–746, 1993.
-
(1993)
, vol.5
, pp. 739-746
-
-
Yuan, J.-L.1
Fine, T.L.2
|