M. Ishikawa, Rule extraction by successive regularization, Neural Networks 13 (2000): 1171-1183. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Extracting Symbolic Rules from Trained Neural Network Ensembles - Zhou, Jiang, Chen (2003) (2 citations) (Correct)
....the activation values of hidden units and repeatedly splitting the network to sub networks. Setiono and Leow [37] divide the activation values of relevant hidden units to two subintervals and then find out the set of relevant connections to those relevant units to construct rules. Ishikawa [19] generates a small number of dominant rules at an earlier stage and less dominant rules or exceptions at later stages with the help of structural learning with forgetting. Function analysis based approaches do not disassemble the architecture of the trained neural networks. Instead, they regard ....
M. Ishikawa, Rule extraction by successive regularization, Neural Networks 13 (2000): 1171-1183.
Noisy Time Series Prediction using a Recurrent Neural.. - Giles, Lawrence, Tsoi (2001) (16 citations) (Correct)
.... reduces the noise, the RNN training becomes more effective, and the symbolic input facilitates the 1 For example, x(t) x(t 1) x(t 2) x(t N 1) form the inputs for a delay embedding of the previous N values of a series [62, 52] 2 Rules can also be extracted from feedforward networks [25, 41, 56, 4, 49, 29], and other types of recurrent neural networks [20] however the recurrent network approach and deterministic finite state automata extraction seem particularly suitable for a time series problem. 2 extraction of rules from the trained networks. Furthermore, it can be argued that the ....
M. Ishikawa. Rule extraction by successive regularization. In International Conference on Neural Networks, pages 1139--1143. IEEE Press, 1996.
Noisy Time Series Prediction using a Recurrent Neural.. - Giles, Lawrence, Tsoi (2000) (16 citations) (Correct)
....high noise and significant non stationarity. In this paper, the noisy times series prediction problem considered is the prediction of foreign exchange rates. A brief overview of foreign exchange rates is presented in the next section. 2 Rules can also be extracted from feedforward networks [25, 41, 56, 4, 49, 29], and other types of recurrent neural networks [20] however the recurrent network approach and deterministic finite state automata extraction seem particularly suitable for a time series problem. 3 1.2 Foreign Exchange Rates The foreign exchange market as of 1997 is the world s largest market, ....
M. Ishikawa. Rule extraction by successive regularization. In International Conference on Neural Networks, pages 1139--1143. IEEE Press, 1996.
Noisy Time Series Prediction using Symbolic Representation .. - Lawrence, Tsoi, Giles (1996) (1 citation) (Correct)
....respect to the US Dollar and is from the Monetary Yearbook of the Chicago Mercantile Exchange. There are 3645 data points for each exchange rate covering the period September 3, 1973 to May 18, 1987. In contrast to Weigend et al. this work 2 Rules can also be extracted from feedforward networks [21, 35, 44, 2, 39, 25], however the recurrent network approach and deterministic finite state automata extraction seem particularly suitable for a time series problem. considers the prediction of all five exchange rates in the data and prediction for all days of the week instead of just Mondays. 3 Fundamental Issues ....
M. Ishikawa. Rule extraction by successive regularization. In International Conference on Neural Networks, pages 1139--1143. IEEE Press, 1996.
Extracting Symbolic Rules from Trained Neural Network Ensembles - Zhou, Jiang, Chen (2003) (2 citations) (Correct)
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M. Ishikawa, Rule extraction by successive regularization, Neural Networks 13 (2000): 1171-1183.
Binary Rule Generation via Hamming Clustering - Muselli, Liberati (2002) (Correct)
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M. Ishikawa, "Rule extraction by successive regularization," Neural Networks, vol. 13, pp. 1171--1183, 2000.
Extracting Symbolic Rules from Trained Neural Network Ensembles - Zhou, Jiang, Chen (2003) (2 citations) (Correct)
No context found.
M. Ishikawa, Rule extraction by successive regularization, Neural Networks 13 (2000): 1171-1183.
Extraction of Crisp Logical Rules Using.. - Duch, Adamczak.. (1997) (10 citations) (Correct)
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M. Ishikawa, Rule extraction by successive regularization, in: Proc. of the 1996 IEEE ICNN, Washington, June 1996, pp. 1139--1143.
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