1999
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy learning for local modelling and control design Journal Article In: International journal of control, vol. 72, no. 7-8, pp. 643-658, 1999, (DOI: 10.1080/002071799220830). @article{info:hdl:2013/69532b,
title = {Lazy learning for local modelling and control design},
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Jansen, Maarten; Uytterhoeven, G.; Bultheel, A. Image de-noising by integer wavelet transforms and generalized cross validation Journal Article In: Medical physics, vol. 26, no. 4, pp. 622-630, 1999, (DOI: 10.1118/1.598562). @article{info:hdl:2013/133296,
title = {Image de-noising by integer wavelet transforms and generalized cross validation},
author = {Maarten Jansen and G. Uytterhoeven and A. Bultheel},
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Birattari, Mauro; Bontempi, Gianluca; Bersini, Hugues Lazy learning meets the recursive least squares algorithm Journal Article In: Advances in neural information processing systems, vol. 11, pp. 375-381, 1999, (Language of publication: en). @article{info:hdl:2013/200953b,
title = {Lazy learning meets the recursive least squares algorithm},
author = {Mauro Birattari and Gianluca Bontempi and Hugues Bersini},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/200953},
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Jansen, Maarten; Bultheel, A. Geometrical Priors for Noisefree Wavelet Coefficient Configurations in Image De-noising Journal Article In: Lecture notes in statistics, vol. 141, pp. 223-242, 1999, (Language of publication: en). @article{info:hdl:2013/133294,
title = {Geometrical Priors for Noisefree Wavelet Coefficient Configurations in Image De-noising},
author = {Maarten Jansen and A. Bultheel},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy learning for local modelling and control design Journal Article In: International journal of control, vol. 72, no. 7-8, pp. 643-658, 1999, (DOI: 10.1080/002071799220830). @article{info:hdl:2013/69532,
title = {Lazy learning for local modelling and control design},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/69532/1/69532.pdf},
year = {1999},
date = {1999-01-01},
journal = {International journal of control},
volume = {72},
number = {7-8},
pages = {643-658},
abstract = {This paper presents local methods for modelling and control of discrete-time unknown non-linear dynamical systems, when only input-output data are available. We propose the adoption of lazy learning, a memory-based technique for local modelling. The modelling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. Also, three methods to design controllers based on the local linearization provided by the lazy learning algorithm are described. In the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take. The second is an indirect method inspired by self-tuning regulators where recursive least squares estimation is replaced by a local approximator. The third method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems about regimes which are far from the equilibrium points. Simulation examples of identification and control of non-linear systems starting from observed data are given.},
note = {DOI: 10.1080/002071799220830},
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This paper presents local methods for modelling and control of discrete-time unknown non-linear dynamical systems, when only input-output data are available. We propose the adoption of lazy learning, a memory-based technique for local modelling. The modelling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. Also, three methods to design controllers based on the local linearization provided by the lazy learning algorithm are described. In the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take. The second is an indirect method inspired by self-tuning regulators where recursive least squares estimation is replaced by a local approximator. The third method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems about regimes which are far from the equilibrium points. Simulation examples of identification and control of non-linear systems starting from observed data are given. |
Birattari, Mauro; Bontempi, Gianluca; Bersini, Hugues Lazy learning meets the recursive least squares algorithm Journal Article In: Advances in neural information processing systems, vol. 11, pp. 375-381, 1999, (Language of publication: en). @article{info:hdl:2013/200953,
title = {Lazy learning meets the recursive least squares algorithm},
author = {Mauro Birattari and Gianluca Bontempi and Hugues Bersini},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/200953},
year = {1999},
date = {1999-01-01},
journal = {Advances in neural information processing systems},
volume = {11},
pages = {375-381},
abstract = {Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered relevant according to a distance measure. In this paper we propose a data-driven method to select on a query-by-query basis the optimal number of neighbors to be considered for each prediction. As an efficient way to identify and validate local models, the recursive least squares algorithm is introduced in the context of local approximation and lazy learning. Furthermore, beside the winner-takes-all strategy for model selection, a local combination of the most promising models is explored. The method proposed is tested on six different datasets and compared with a state-of-the-art approach.},
note = {Language of publication: en},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered relevant according to a distance measure. In this paper we propose a data-driven method to select on a query-by-query basis the optimal number of neighbors to be considered for each prediction. As an efficient way to identify and validate local models, the recursive least squares algorithm is introduced in the context of local approximation and lazy learning. Furthermore, beside the winner-takes-all strategy for model selection, a local combination of the most promising models is explored. The method proposed is tested on six different datasets and compared with a state-of-the-art approach. |
Bontempi, Gianluca; Birattari, Mauro A bound on the cross-validation estimate for algorithm assessment Proceedings Article In: Postma, Eric; Gyssens, Marc (Ed.): BNAIC'99: proceedings of the eleventh Belgium-Netherlands Artificial Intelligence Conference, pp. 115-122, 1999, (Conference: (November 3-4, 1999: Universiteit Maastricht, Maastricht, The Netherlands)). @inproceedings{info:hdl:2013/73389,
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Local learning for iterated time-series prediction Proceedings Article In: Bratko, I; Dzeroski, S (Ed.): Machine learning: proceedings of the Sixteenth International Conference (ICML'99), pp. 32-38, Morgan Kaufmann Publishers, 1999, (Conference: (June 27-30, 1999: Bled, Slovenia)). @inproceedings{info:hdl:2013/76127,
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy Learners at work: the Lazy Learning Toolbox Proceedings Article In: Zimmerman, Hans-Jürgen (Ed.): EUFIT '99: 7th European Congress on Intelligent Techniques and Soft Computing, Aachen, 1999, (Conference: (September 13-16, 1999: Aachen, Germany)). @inproceedings{info:hdl:2013/76128,
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Remortel, Nick Van; Lenaerts, Tom; Manderick, Bernard The evolution of ROBDDs: preliminary results and a first analysis Proceedings Article In: Proceedings of the 4th International Conference on Evolvable Hardware, pp. 249, Springer, 1999, (Language of publication: en). @inproceedings{info:hdl:2013/205984b,
title = {The evolution of ROBDDs: preliminary results and a first analysis},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Local learning for iterated time-series prediction Proceedings Article In: Bratko, I.; Dzeroski, S. (Ed.): Machine learning: proceedings of the Sixteenth International Conference (ICML'99), pp. 32-38, Morgan Kaufmann Publishers, 1999, (Conference: (June 27-30, 1999: Bled, Slovenia)). @inproceedings{info:hdl:2013/76127b,
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy Learners at work: the Lazy Learning Toolbox Proceedings Article In: Zimmerman, Hans-Jürgen (Ed.): EUFIT '99: 7th European Congress on Intelligent Techniques and Soft Computing, Aachen, 1999, (Conference: (September 13-16, 1999: Aachen, Germany)). @inproceedings{info:hdl:2013/76128b,
title = {Lazy Learners at work: the Lazy Learning Toolbox},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
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Bontempi, Gianluca; Birattari, Mauro A bound on the cross-validation estimate for algorithm assessment Proceedings Article In: Postma, Eric; Gyssens, Marc (Ed.): BNAIC’99: proceedings of the eleventh Belgium-Netherlands Artificial Intelligence Conference, pp. 115-122, 1999, (Conference: (November 3-4, 1999: Universiteit Maastricht, Maastricht, The Netherlands)). @inproceedings{info:hdl:2013/73389b,
title = {A bound on the cross-validation estimate for algorithm assessment},
author = {Gianluca Bontempi and Mauro Birattari},
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Remortel, Nick Van; Lenaerts, Tom; Manderick, Bernard The evolution of ROBDDs: preliminary results and a first analysis Proceedings Article In: Proceedings of the 4th International Conference on Evolvable Hardware, pp. 249, Springer, 1999, (Language of publication: en). @inproceedings{info:hdl:2013/205984,
title = {The evolution of ROBDDs: preliminary results and a first analysis},
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1998
|
Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Recursive lazy learning for modeling and control Journal Article In: Lecture notes in computer science, vol. 1398, pp. 292-303, 1998, (Language of publication: en). @article{info:hdl:2013/73386b,
title = {Recursive lazy learning for modeling and control},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/73386},
year = {1998},
date = {1998-01-01},
journal = {Lecture notes in computer science},
volume = {1398},
pages = {292-303},
note = {Language of publication: en},
keywords = {},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Recursive lazy learning for modeling and control Journal Article In: Lecture notes in computer science, vol. 1398, pp. 292-303, 1998, (Language of publication: en). @article{info:hdl:2013/73386,
title = {Recursive lazy learning for modeling and control},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/73386/1/73386.pdf},
year = {1998},
date = {1998-01-01},
journal = {Lecture notes in computer science},
volume = {1398},
pages = {292-303},
abstract = {This paper presents a local method for modeling and control of non-linear dynamical systems from input-output data. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear optimal control theory. The local modeling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The control method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems in configurations which are far from equilibrium. Simulations of the identification of a non-linear benchmark model and of the control of a complex non-linear system (the bioreactor) are presented. The experimental results show that the approach can obtain better performance than neural networks in identification and control, even using smaller training data sets. copyright Springer-Verlag Berlin Heidelberg 1998.},
note = {Language of publication: en},
keywords = {},
pubstate = {published},
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}
This paper presents a local method for modeling and control of non-linear dynamical systems from input-output data. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear optimal control theory. The local modeling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The control method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems in configurations which are far from equilibrium. Simulations of the identification of a non-linear benchmark model and of the control of a complex non-linear system (the bioreactor) are presented. The experimental results show that the approach can obtain better performance than neural networks in identification and control, even using smaller training data sets. copyright Springer-Verlag Berlin Heidelberg 1998. |
Bontempi, Gianluca Local methods for nonlinear control Proceedings Article In: FAMIMO Workshop on Local Modeling and Local Control, 1998, (Conference: (25 March 1998: Seville, Spain)). @inproceedings{info:hdl:2013/76140,
title = {Local methods for nonlinear control},
author = {Gianluca Bontempi},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/76140},
year = {1998},
date = {1998-01-01},
booktitle = {FAMIMO Workshop on Local Modeling and Local Control},
note = {Conference: (25 March 1998: Seville, Spain)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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|
Birattari, Mauro; Bontempi, Gianluca; Bersini, Hugues Local learning for data analysis Proceedings Article In: Verdenius, F; den Broeck, Wouter Van (Ed.): Benelearn'98: Proceedings of the 8th Belgian-Dutch Conference on Machine Learning, pp. 55-61, 1998, (Conference: (1998: ATO-DLO, Wageningen, The Netherlands)). @inproceedings{info:hdl:2013/73391,
title = {Local learning for data analysis},
author = {Mauro Birattari and Gianluca Bontempi and Hugues Bersini},
editor = {F Verdenius and Wouter Van den Broeck},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/73391},
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date = {1998-01-01},
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|
Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy learning for iterated time-series prediction Proceedings Article In: Suykens, J A K; Vandewalle, Jean Claude (Ed.): Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling with Time-Series Prediction Modeling, pp. 62-68, 1998, (Conference: (July 8-10, 1998: Katholieke Universiteit Leuven, Belgium)). @inproceedings{info:hdl:2013/73390,
title = {Lazy learning for iterated time-series prediction},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
editor = {J A K Suykens and Jean Claude Vandewalle},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/73390/1/73390.pdf},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Local learning for nonlinear control Proceedings Article In: Huijberts, H J C; der Schaft, A J Van; Scherpen, J M A (Ed.): Nonlinear control systems design symposium 1998: a proceedings volume from the 4th IFAC Symposium, pp. 360-365, Published for the International Federation of Automatic Control by Pergamon, 1998, (Conference: NOLCOS'98(1-3 July 1998: University of Twente, Enschede, The Netherlands)). @inproceedings{info:hdl:2013/73392,
title = {Local learning for nonlinear control},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
editor = {H J C Huijberts and A J Van der Schaft and J M A Scherpen},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/73392/5/6515745c-589a-4539-8369-7840c2f9bf63.txt},
year = {1998},
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Bersini, Hugues; Birattari, Mauro; Bontempi, Gianluca Adaptive memory based regression methods Proceedings Article In: The 1998 IEEE International Joint Conference on Neural Networks: proceedings : IEEE World Congress on Computational Intelligence, pp. 2402-2406, IEEE, 1998, (Conference: (May 4-May 9, 1998: Anchorage, Alaska, USA)). @inproceedings{info:hdl:2013/76123,
title = {Adaptive memory based regression methods},
author = {Hugues Bersini and Mauro Birattari and Gianluca Bontempi},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/76123},
year = {1998},
date = {1998-01-01},
booktitle = {The 1998 IEEE International Joint Conference on Neural Networks: proceedings : IEEE World Congress on Computational Intelligence},
pages = {2402-2406},
publisher = {IEEE},
abstract = {The task of approximating a non linear mapping using a limited number of observations, asks the data analyst to make several choices involving the set of relevant variables and observations, the learning algorithm, and the validation protocol. In the case of models which are linear in the parameters (e.g. polynomials), statistical theory and economical cross-validation methods provide fast and effective ways to support these choices. However, when pure approximation performance is at stake, a unique linear structure to cover the whole range of data, is often far from optimal. Memory-based methods in contrast are well known to considerably improve the approximation performance, since all the regression analysis is done locally and repeated for each new query. In this paper, we discuss the use of these cross-validation procedures for selecting the features, the neighbors and the polynomial degree for each prediction. The possible automation of these selections on a query basis provides memory-based methods (generally not used in such a flexible way) with a larger degree of adaptivity. Experimental results in time series prediction are presented.},
note = {Conference: (May 4-May 9, 1998: Anchorage, Alaska, USA)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The task of approximating a non linear mapping using a limited number of observations, asks the data analyst to make several choices involving the set of relevant variables and observations, the learning algorithm, and the validation protocol. In the case of models which are linear in the parameters (e.g. polynomials), statistical theory and economical cross-validation methods provide fast and effective ways to support these choices. However, when pure approximation performance is at stake, a unique linear structure to cover the whole range of data, is often far from optimal. Memory-based methods in contrast are well known to considerably improve the approximation performance, since all the regression analysis is done locally and repeated for each new query. In this paper, we discuss the use of these cross-validation procedures for selecting the features, the neighbors and the polynomial degree for each prediction. The possible automation of these selections on a query basis provides memory-based methods (generally not used in such a flexible way) with a larger degree of adaptivity. Experimental results in time series prediction are presented. |
Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy learning for control design Proceedings Article In: 6th European Symposium on Artificial Neural Networks: ESANN '98 : proceedings, pp. 73-78, D facto, 1998, (Conference: (April 22-23-24, 1998: Bruges, Belgium)). @inproceedings{info:hdl:2013/76124,
title = {Lazy learning for control design},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/76124/4/7764b6db-fa82-4789-b2b7-ac06fc7bd6eb.txt},
year = {1998},
date = {1998-01-01},
booktitle = {6th European Symposium on Artificial Neural Networks: ESANN '98 : proceedings},
pages = {73-78},
publisher = {D facto},
note = {Conference: (April 22-23-24, 1998: Bruges, Belgium)},
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Bersini, Hugues; Bontempi, Gianluca; Birattari, Mauro Is readability compatible with accuracy ? From Neuro-Fuzzy to Lazy Learning Proceedings Article In: Brauer, Wilfried (Ed.): Fuzzy-Neuro Systems '98 - Computational Intelligence: proceedings of the 5. International Workshop "Fuzzy-Neuro Systems '98" (FNS '98), pp. 10-25, Infix, 1998, (Conference: (March 19-20, 1998: Munich, Germany)). @inproceedings{info:hdl:2013/76125,
title = {Is readability compatible with accuracy ? From Neuro-Fuzzy to Lazy Learning},
author = {Hugues Bersini and Gianluca Bontempi and Mauro Birattari},
editor = {Wilfried Brauer},
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Lenaerts, Tom; Manderick, Bernard Building a genetic programming framework: The added-value of design patterns Proceedings Article In: Proceedings of the First European Workshop on Genetic Programming, pp. 196-208, Springer, 1998, (Language of publication: en). @inproceedings{info:hdl:2013/205983b,
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Bersini, Hugues; Birattari, Mauro; Bontempi, Gianluca Adaptive memory based regression methods Proceedings Article In: The 1998 IEEE International Joint Conference on Neural Networks: proceedings : IEEE World Congress on Computational Intelligence, pp. 2402-2406, IEEE, 1998, (Conference: (May 4-May 9, 1998: Anchorage, Alaska, USA)). @inproceedings{info:hdl:2013/76123b,
title = {Adaptive memory based regression methods},
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year = {1998},
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Bersini, Hugues; Bontempi, Gianluca; Birattari, Mauro Is readability compatible with accuracy ? From Neuro-Fuzzy to Lazy Learning Proceedings Article In: Brauer, Wilfried (Ed.): Fuzzy-Neuro Systems '98 - Computational Intelligence: proceedings of the 5. International Workshop "Fuzzy-Neuro Systems '98" (FNS '98), pp. 10-25, Infix, 1998, (Conference: (March 19-20, 1998: Munich, Germany)). @inproceedings{info:hdl:2013/76125b,
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Bontempi, Gianluca Local methods for nonlinear control Proceedings Article In: FAMIMO Workshop on Local Modeling and Local Control, 1998, (Conference: (25 March 1998: Seville, Spain)). @inproceedings{info:hdl:2013/76140b,
title = {Local methods for nonlinear control},
author = {Gianluca Bontempi},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/76140},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy learning for control design Proceedings Article In: 6th European Symposium on Artificial Neural Networks: ESANN '98 : proceedings, pp. 73-78, D facto, 1998, (Conference: (April 22-23-24, 1998: Bruges, Belgium)). @inproceedings{info:hdl:2013/76124b,
title = {Lazy learning for control design},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
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Birattari, Mauro; Bontempi, Gianluca; Bersini, Hugues Local learning for data analysis Proceedings Article In: Verdenius, F.; den Broeck, Wouter Van (Ed.): Benelearn’98: Proceedings of the 8th Belgian-Dutch Conference on Machine Learning, pp. 55-61, 1998, (Conference: (1998: ATO-DLO, Wageningen, The Netherlands)). @inproceedings{info:hdl:2013/73391b,
title = {Local learning for data analysis},
author = {Mauro Birattari and Gianluca Bontempi and Hugues Bersini},
editor = {F. Verdenius and Wouter Van den Broeck},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Lazy learning for iterated time-series prediction Proceedings Article In: Suykens, J. A. K.; Vandewalle, Jean Claude (Ed.): Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling with Time-Series Prediction Modeling, pp. 62-68, 1998, (Conference: (July 8-10, 1998: Katholieke Universiteit Leuven, Belgium)). @inproceedings{info:hdl:2013/73390b,
title = {Lazy learning for iterated time-series prediction},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
editor = {J. A. K. Suykens and Jean Claude Vandewalle},
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Bontempi, Gianluca; Birattari, Mauro; Bersini, Hugues Local learning for nonlinear control Proceedings Article In: Huijberts, H J C; der Schaft, A. J. Van; Scherpen, J. M. A. (Ed.): Nonlinear control systems design symposium 1998: a proceedings volume from the 4th IFAC Symposium, pp. 360-365, Published for the International Federation of Automatic Control by Pergamon, 1998, (Conference: NOLCOS’98(1-3 July 1998: University of Twente, Enschede, The Netherlands)). @inproceedings{info:hdl:2013/73392b,
title = {Local learning for nonlinear control},
author = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
editor = {H J C Huijberts and A. J. Van der Schaft and J. M. A. Scherpen},
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Lenaerts, Tom; Manderick, Bernard Building a genetic programming framework: The added-value of design patterns Proceedings Article In: Proceedings of the First European Workshop on Genetic Programming, pp. 196-208, Springer, 1998, (Language of publication: en). @inproceedings{info:hdl:2013/205983,
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1997
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Bersini, Hugues; Bontempi, Gianluca Now comes the time to defuzzify neuro-fuzzy models Journal Article In: Fuzzy sets and systems, vol. 90, no. 2, pp. 161-169, 1997, (DOI: 10.1016/S0165-0114(97)00082-1). @article{info:hdl:2013/69533b,
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Jansen, Maarten; Malfait, M; Bultheel, A. Generalized Cross Validation for wavelet thresholding Journal Article In: Signal processing, vol. 56, no. 1, pp. 33-44, 1997, (Language of publication: en). @article{info:hdl:2013/133293,
title = {Generalized Cross Validation for wavelet thresholding},
author = {Maarten Jansen and M Malfait and A. Bultheel},
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Bersini, Hugues; Bontempi, Gianluca Now comes the time to defuzzify neuro-fuzzy models Journal Article In: Fuzzy sets and systems, vol. 90, no. 2, pp. 161-169, 1997, (DOI: 10.1016/S0165-0114(97)00082-1). @article{info:hdl:2013/69533,
title = {Now comes the time to defuzzify neuro-fuzzy models},
author = {Hugues Bersini and Gianluca Bontempi},
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abstract = {Fuzzy models present a singular Janus-face: On the one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like "low-order interpolation" and "universal function approximation". Neuro-fuzzy basically provides fuzzy models with the capacity, based on the available data, to compensate for the missing human knowledge by an automatic self-tuning of the structure and the parameters. A first consequence of this hybridization between the architectural and representational aspect of fuzzy models and the learning mechanisms of neural networks has been to progressively increase and fuzzify the contrast between the two Janus faces: readability or performance. copyright 1997 Elsevier Science B.V.},
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Fuzzy models present a singular Janus-face: On the one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like "low-order interpolation" and "universal function approximation". Neuro-fuzzy basically provides fuzzy models with the capacity, based on the available data, to compensate for the missing human knowledge by an automatic self-tuning of the structure and the parameters. A first consequence of this hybridization between the architectural and representational aspect of fuzzy models and the learning mechanisms of neural networks has been to progressively increase and fuzzify the contrast between the two Janus faces: readability or performance. copyright 1997 Elsevier Science B.V. |
Rizzoli, Andrea Emilio; Gambardella, Luca Maria; Bontempi, Gianluca; Mastrolilli, Monaldo M Simulation of an intermodal container terminal to assist the management in the decision making process Proceedings Article In: Proceedings of the International Congress on Modelling and Simulation MODSIM'97, 1997, (Conference: (8-11 December 1997: University of Tasmania, Hobart)). @inproceedings{info:hdl:2013/76122,
title = {Simulation of an intermodal container terminal to assist the management in the decision making process},
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Bontempi, Gianluca; Bersini, Hugues Identification of a sensor model with hybrid neuro-fuzzy methods Proceedings Article In: Bulsari, A B; Kallio, S (Ed.): Proceedings of the 1997 International Conference on Engineering Applications of Neural Networks (EANN '97): Neural Networks in Engineering systems, pp. 325-328, 1997, (Conference: International Conference on Engineering Applications of Neural Networks(Stockolm, Sweden)). @inproceedings{info:hdl:2013/76121,
title = {Identification of a sensor model with hybrid neuro-fuzzy methods},
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Bersini, Hugues; Birattari, Mauro; Bontempi, Gianluca Combining Dynamic Programming and Neurocontrol: Some Basic Issues Proceedings Article In: Bersini, Hugues; Dorigo, Marco; Glorennec, P Y; Jouffe, L; Anquetil, E (Ed.): Third European Workshop on Reinforcement Learning, 1997, (Conference: (October 13-14: Rennes, France)). @inproceedings{info:hdl:2013/73394,
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editor = {Hugues Bersini and Marco Dorigo and P Y Glorennec and L Jouffe and E Anquetil},
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Bontempi, Gianluca; Birattari, Mauro Lazy learning indirect control for discrete time non-linear systems Proceedings Article In: Johansen, T A; Foss, B A (Ed.): Abstracts of Workshop on Multiple Model Approaches to Modelling and Control, pp. 72-80, 1997, (Conference: (Trondheim, Norway)). @inproceedings{info:hdl:2013/73393,
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author = {Gianluca Bontempi and Mauro Birattari},
editor = {T A Johansen and B A Foss},
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Bontempi, Gianluca; Gambardella, Luca Maria; Rizzoli, Andrea Emilio Simulation and optimisation for management of intermodal terminals Proceedings Article In: Kaylan, Ali Riza; Lehmann, Axel (Ed.): 11th European Simulation Multiconference, ESM '97: gaining competitive advantage through simulation technologies, pp. 646-652, SCS International, 1997, (Conference: (June 1-4, 1997: Bou gazic ci University, Istanbul)). @inproceedings{info:hdl:2013/76119,
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Bersini, Hugues; Bontempi, Gianluca Fuzzy models viewed as multi-expert networks Proceedings Article In: IFSA'97 Prague: proceedings seventh International Fuzzy Systems Association World Congress, pp. 354-359, Academia, 1997, (Conference: (June 25-29, 1997: University of Economics, Prague, Czech Republic)). @inproceedings{info:hdl:2013/76118,
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Bersini, Hugues; Bontempi, Gianluca Now comes the time to defuzzify neuro-fuzzy models Proceedings Article In: Intelligent components and instruments for control applications 1997 (SICICA '97): a proceedings volume from the 3rd IFAC Symposium, pp. 53-58, Pergamon, 1997, (Conference: (9-11 June 1997: Annecy, France)). @inproceedings{info:hdl:2013/76120,
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Lenaerts, Tom; Deridder, Dirk; Manderick, Bernard Genetic programming and data mining: an empirical evaluation of basic functionalities Proceedings Article In: Proceedings of the 9th Benelux Conference on Artificial intelligence, pp. 59-68, 1997, (Conference: BNAIC). @inproceedings{info:hdl:2013/243606,
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Lenaerts, Tom; Deridder, Dirk; Manderick, Bernard Genetic programming and data mining: an empirical evaluation of basic functionalities Proceedings Article In: Proceedings of the 9th Benelux Conference on Artificial intelligence, pp. 59-68, 1997, (Conference: BNAIC). @inproceedings{info:hdl:2013/243606b,
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Rizzoli, Andrea Emilio; Gambardella, Luca Maria; Bontempi, Gianluca; Mastrolilli, Monaldo M. Simulation of an intermodal container terminal to assist the management in the decision making process Proceedings Article In: Proceedings of the International Congress on Modelling and Simulation MODSIM'97, 1997, (Conference: (8-11 December 1997: University of Tasmania, Hobart)). @inproceedings{info:hdl:2013/76122b,
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Bontempi, Gianluca; Gambardella, Luca Maria; Rizzoli, Andrea Emilio Simulation and optimisation for management of intermodal terminals Proceedings Article In: Kaylan, Ali Riza; Lehmann, Axel (Ed.): 11th European Simulation Multiconference, ESM '97: gaining competitive advantage through simulation technologies, pp. 646-652, SCS International, 1997, (Conference: (June 1-4, 1997: Boğaziçi University, Istanbul)). @inproceedings{info:hdl:2013/76119b,
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Bersini, Hugues; Bontempi, Gianluca Fuzzy models viewed as multi-expert networks Proceedings Article In: IFSA'97 Prague: proceedings seventh International Fuzzy Systems Association World Congress, pp. 354-359, Academia, 1997, (Conference: (June 25-29, 1997: University of Economics, Prague, Czech Republic)). @inproceedings{info:hdl:2013/76118b,
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Bersini, Hugues; Bontempi, Gianluca Now comes the time to defuzzify neuro-fuzzy models Proceedings Article In: Intelligent components and instruments for control applications 1997 (SICICA '97): a proceedings volume from the 3rd IFAC Symposium, pp. 53-58, Pergamon, 1997, (Conference: (9-11 June 1997: Annecy, France)). @inproceedings{info:hdl:2013/76120b,
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Bontempi, Gianluca; Bersini, Hugues Identification of a sensor model with hybrid neuro-fuzzy methods Proceedings Article In: Bulsari, A. B.; Kallio, S. (Ed.): Proceedings of the 1997 International Conference on Engineering Applications of Neural Networks (EANN '97): Neural Networks in Engineering systems, pp. 325-328, 1997, (Conference: International Conference on Engineering Applications of Neural Networks(Stockolm, Sweden)). @inproceedings{info:hdl:2013/76121b,
title = {Identification of a sensor model with hybrid neuro-fuzzy methods},
author = {Gianluca Bontempi and Hugues Bersini},
editor = {A. B. Bulsari and S. Kallio},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/76121},
year = {1997},
date = {1997-01-01},
booktitle = {Proceedings of the 1997 International Conference on Engineering Applications of Neural Networks (EANN '97): Neural Networks in Engineering systems},
pages = {325-328},
note = {Conference: International Conference on Engineering Applications of Neural Networks(Stockolm, Sweden)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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Bontempi, Gianluca; Birattari, Mauro Lazy learning indirect control for discrete time non-linear systems Proceedings Article In: Johansen, T. A.; Foss, B. A. (Ed.): Abstracts of Workshop on Multiple Model Approaches to Modelling and Control, pp. 72-80, 1997, (Conference: (Trondheim, Norway)). @inproceedings{info:hdl:2013/73393b,
title = {Lazy learning indirect control for discrete time non-linear systems},
author = {Gianluca Bontempi and Mauro Birattari},
editor = {T. A. Johansen and B. A. Foss},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/73393},
year = {1997},
date = {1997-01-01},
booktitle = {Abstracts of Workshop on Multiple Model Approaches to Modelling and Control},
pages = {72-80},
note = {Conference: (Trondheim, Norway)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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