-
1
-
-
80052251399
-
Tree-based variable selection for dimensionality reduction of large-scale control systems
-
IEEE, April
-
A. Castelletti, S. Galelli, M. Restelli, and R. Soncini-Sessa. Tree-based variable selection for dimensionality reduction of large-scale control systems. In IEEE Symposium on ADPRL, pages 62-69. IEEE, April 2011.
-
(2011)
IEEE Symposium on ADPRL
, pp. 62-69
-
-
Castelletti, A.1
Galelli, S.2
Restelli, M.3
Soncini-Sessa, R.4
-
3
-
-
84880666558
-
Solving non-markovian control tasks with neuroevolution
-
Morgan Kaufmann
-
F. J. Gomez and R. Miikkulainen. Solving non-markovian control tasks with neuroevolution. In In Proceedings of the 16th IJCAI, pages 1356-1361. Morgan Kaufmann, 1999.
-
(1999)
In Proceedings of the 16th IJCAI
, pp. 1356-1361
-
-
Gomez, F.J.1
Miikkulainen, R.2
-
5
-
-
78049334876
-
Feature selection for reinforcement learning: Evaluating implicit state-reward dependency via conditional mutual information
-
H. Hachiya and M. Sugiyama. Feature selection for reinforcement learning: Evaluating implicit state-reward dependency via conditional mutual information. In Proceedings of ECML, pages 474-489, 2010.
-
(2010)
Proceedings of ECML
, pp. 474-489
-
-
Hachiya, H.1
Sugiyama, M.2
-
8
-
-
17044405923
-
Toward integrating feature selection algorithms for classification and clustering
-
H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4):491-502, 2005.
-
(2005)
IEEE Transactions on Knowledge and Data Engineering
, vol.17
, Issue.4
, pp. 491-502
-
-
Liu, H.1
Yu, L.2
-
9
-
-
77955655312
-
Dimension reduction and its application to model-based exploration in continuous spaces
-
10.1007/s10994-010-5202-y
-
A. Nouri and M. Littman. Dimension reduction and its application to model-based exploration in continuous spaces. Machine Learning, 81:85-98, 2010. 10.1007/s10994-010-5202-y.
-
(2010)
Machine Learning
, vol.81
, pp. 85-98
-
-
Nouri, A.1
Littman, M.2
-
10
-
-
24344458137
-
Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
-
H. Peng, F. Long, and C. Ding. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27:1226-1238, 2005.
-
(2005)
IEEE Trans. on Pattern Analysis and Machine Intelligence
, vol.27
, pp. 1226-1238
-
-
Peng, H.1
Long, F.2
Ding, C.3
-
11
-
-
0042125798
-
Efficient reinforcement learning through evolving neural network topologies
-
San Francisco, Morgan Kaufmann
-
K. O. Stanley and R. Miikkulainen. Efficient reinforcement learning through evolving neural network topologies. In Proceedings of GECCO, page 9, San Francisco, 2002. Morgan Kaufmann.
-
(2002)
Proceedings of GECCO
, pp. 9
-
-
Stanley, K.O.1
Miikkulainen, R.2
-
13
-
-
84865033087
-
Automated feature selection in neuroevolution
-
M. Tan, M. Hartley, M. Bister, and R. Deklerck. Automated feature selection in neuroevolution. Evolutionary Intelligence, 1(4):271-292, 2009.
-
(2009)
Evolutionary Intelligence
, vol.1
, Issue.4
, pp. 271-292
-
-
Tan, M.1
Hartley, M.2
Bister, M.3
Deklerck, R.4
-
14
-
-
33646714634
-
Evolutionary function approximation for reinforcement learning
-
May
-
S. Whiteson and P. Stone. Evolutionary function approximation for reinforcement learning. Journal of Machine Learning Research, 7:877-917, May 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 877-917
-
-
Whiteson, S.1
Stone, P.2
-
16
-
-
79960132242
-
Embedded incremental feature selection for reinforcement learning
-
R. Wright, S. Loscalzo, and L. Yu. Embedded incremental feature selection for reinforcement learning. In ICAART 2011, pages 263-268, 2011.
-
(2011)
ICAART 2011
, pp. 263-268
-
-
Wright, R.1
Loscalzo, S.2
Yu, L.3
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