-
2
-
-
0029484103
-
Survey and critique of techniques for extracting rules from trained artificical neural networks
-
R. Andrews, J. Diederich, and A.B. Tickle. Survey and critique of techniques for extracting rules from trained artificical neural networks. Knowledge Based Systems, 8:378–389, 1995.
-
(1995)
Knowledge Based Systems
, vol.8
, pp. 378-389
-
-
Andrews, R.1
Diederich, J.2
Tickle, A.B.3
-
6
-
-
0035271419
-
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
-
W. Duch, R. Adamczak, and K. Grabczewski. A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks, 11:277–306, 2000.
-
(2000)
IEEE Transactions on Neural Networks
, vol.11
, pp. 277-306
-
-
Duch, W.1
Adamczak, R.2
Grabczewski, K.3
-
9
-
-
0028543366
-
Training feedforward networks with the Marquadt algorithm
-
M.T. Hagan and M. Menhaj. Training feedforward networks with the Marquadt algorithm. IEEE Transactions on Neural Networks, 5:989–993, 1994.
-
(1994)
IEEE Transactions on Neural Networks
, vol.5
, pp. 989-993
-
-
Hagan, M.T.1
Menhaj, M.2
-
12
-
-
84949740922
-
Ordering of neural network architectures
-
M. Holeňa. Ordering of neural network architectures. Neural Network World, 3:131–160, 1993.
-
(1993)
Neural Network World
, vol.3
, pp. 131-160
-
-
Holeňa, M.1
-
13
-
-
0028754281
-
Lattices of neural network architectures
-
M. Holeňa. Lattices of neural network architectures. Neural Network World, 4:435–464, 1994.
-
(1994)
Neural Network World
, vol.4
, pp. 435-464
-
-
Holeňa, M.1
-
14
-
-
84974695520
-
Observational logic integrates data mining based on statistics and neural networks
-
D.A. Zighed, J. Komorowski, and J.M. Żytkov, editors, Springer Verlag, Berlin
-
M. Holeňa. Observational logic integrates data mining based on statistics and neural networks. In D.A. Zighed, J. Komorowski, and J.M. Żytkov, editors, Principles of Data Mining and Knowledge Discovery, pages 440–445. Springer Verlag, Berlin, 2000.
-
(2000)
Principles of Data Mining and Knowledge Discovery
, pp. 440-445
-
-
Holeňa, M.1
-
16
-
-
0001307541
-
Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives
-
K. Hornik, M. Stinchcombe, H. White, and P. Auer. Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives. Neural Computation, 6:1262–1275, 1994.
-
(1994)
Neural Computation
, vol.6
, pp. 1262-1275
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
Auer, P.4
-
17
-
-
0033082449
-
Using input parameter influences to support the decisions of feed-forward neural networks
-
P. Howes and N. Crook. Using input parameter influences to support the decisions of feed-forward neural networks. Neurocomputing, 24:191–206, 1999.
-
(1999)
Neurocomputing
, vol.24
, pp. 191-206
-
-
Howes, P.1
Crook, N.2
-
18
-
-
0034551785
-
Rule extraction by successive regularization
-
M. Ishikawa. Rule extraction by successive regularization. Neural Networks, 13:1171–1183, 2000.
-
(2000)
Neural Networks
, vol.13
, pp. 1171-1183
-
-
Ishikawa, M.1
-
19
-
-
0026627415
-
Kolmogorov’s theorem and multilayer neural networks
-
V. Kůrková. Kolmogorov’s theorem and multilayer neural networks. Neural Networks, 5:501–506, 1992.
-
(1992)
Neural Networks
, vol.5
, pp. 501-506
-
-
Kůrková, V.1
-
20
-
-
0027262895
-
Multilayer feedforward networks with a non-polynomial activation can approximate any function
-
M. Leshno, V.Y. Lin, A. Pinkus, and S. Schocken. Multilayer feedforward networks with a non-polynomial activation can approximate any function. Neural Networks, 6:861–867, 1993.
-
(1993)
Neural Networks
, vol.6
, pp. 861-867
-
-
Leshno, M.1
Lin, V.Y.2
Pinkus, A.3
Schocken, S.4
-
21
-
-
0242427979
-
Bounds for the computational power and learning complexity of analog neural nets
-
W. Maass. Bounds for the computational power and learning complexity of analog neural nets. SIAM Journal on Computing, 26:708–732, 1997.
-
(1997)
SIAM Journal on Computing
, vol.26
, pp. 708-732
-
-
Maass, W.1
-
22
-
-
0032812327
-
Rule-extraction by backpropagation of polyhedra
-
F. Maire. Rule-extraction by backpropagation of polyhedra. Neural Networks, 12:717–725, 1999.
-
(1999)
Neural Networks
, vol.12
, pp. 717-725
-
-
Maire, F.1
-
23
-
-
0032236619
-
On the complexity of recognizing regions computable by two-layered perceptrons
-
E.N. Mayoraz. On the complexity of recognizing regions computable by two-layered perceptrons. Annals of Mathematics and Artificial Intelligence, 24:129–153, 1998.
-
(1998)
Annals of Mathematics and Artificial Intelligence
, vol.24
, pp. 129-153
-
-
Mayoraz, E.N.1
-
24
-
-
0031274113
-
Knowledge-based fuzzy MLP for classification and rule generation
-
S. Mitra, R.K. De, and S.K. Pal. Knowledge-based fuzzy MLP for classification and rule generation. IEEE Transactions on Neural Networks, 8:1338–1350, 1997.
-
(1997)
IEEE Transactions on Neural Networks
, vol.8
, pp. 1338-1350
-
-
Mitra, S.1
De, R.K.2
Pal, S.K.3
-
25
-
-
0034187785
-
Neuro-fuzzy rule generation: Survey in soft computing framework
-
S. Mitra and Y. Hayashi. Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks, 11:748–768, 2000.
-
(2000)
IEEE Transactions on Neural Networks
, vol.11
, pp. 748-768
-
-
Mitra, S.1
Hayashi, Y.2
-
27
-
-
0000646059
-
Learning internal representations by error backpropagation
-
D.E. Rumelhart and J.L. McClelland, editors
-
D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning internal representations by error backpropagation. In D.E. Rumelhart and J.L. McClelland, editors, Parallel Distributed Processing: Experiments in the Microstructure of Cognition, pages 318–362, 1986.
-
(1986)
Parallel Distributed Processing: Experiments in the Microstructure of Cognition
, pp. 318-362
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
28
-
-
0032208720
-
The truth will come to light: Directions and challenges in extracting rules from trained artificial neural networks
-
A.B. Tickle, R. Andrews, M. Golea, and J. Diederich. The truth will come to light: Directions and challenges in extracting rules from trained artificial neural networks. IEEE Transactions on Neural Networks, 9:1057–1068, 1998.
-
(1998)
IEEE Transactions on Neural Networks
, vol.9
, pp. 1057-1068
-
-
Tickle, A.B.1
Andrews, R.2
Golea, M.3
Diederich, J.4
-
29
-
-
0033742671
-
Extracting rules from trained neural networks
-
H. Tsukimoto. Extracting rules from trained neural networks. IEEE Transactions on Neural Networks, 11:333–389, 2000.
-
(2000)
IEEE Transactions on Neural Networks
, vol.11
, pp. 333-389
-
-
Tsukimoto, H.1
|