Searching for authors named "Amaury Lendasse" – sorted by Relevance.

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19 documents found, showing 1 through 10. Next 10 →

Fast bootstrap applied to LS-SVM for long term prediction of time series
by Amaury Lendasse — 2004 — In Proceedings of the 2004 IEEE International Joint Conference on Neural Networks
…Abstract- Time series forecasting is usually limited to one-step ahead prediction. This goal is extended here to longer-term prediction, obtained using the least-square support vector machines model. The influence of the model parameters is observed when the time horizon of the prediction is increas…
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Measures of Topological Relevance based on the Self-Organizing Map: Applications to Process Monitoring from Spectroscopic Measurements
by Francesco Corona, Amaury Lendasse
…In this work, the problem of real-time monitoring of products ’ properties from spectrophotoscopic measurements is presented. Light absorbance spectra are used as inputs to soft sensors that estimate outputs otherwise difficult to measure on-line. To overcome the issues associated to calibrating the…
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Long-Term Time Series Forecasting Using Self-Organizing Maps: the Double Vector Quantization Method
by Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Université Paris — 2003
…Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method specifically designed for long-term trends prediction, with a double appli…
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Model Selection with Cross-Validations and Bootstraps - Application to Time Series Prediction with RBFN Models
by Amaury Lendasse, Vincent Wertz, Michel Verleysen — 2003 — Artificial Neural Networks and Neural Information Processing – ICANN/ICONIP 2003
…This paper compares several model selection methods, based on experimental estimates of their generalization errors. Experiments in the context of nonlinear time series prediction by Radial-Basis Function Networks show the superiority of the bootstrap methodology over classical cross-validations …
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A.: Input Selection for Long-Term Prediction of Time-Series
by Jarkko Tikka, Jaakko Hollmén, Amaury Lendasse — 2005 — In: Proceedings of the 8th International Work-Conference on Artificial Neural Networks (IWANN
…Abstract. Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and difficult task of long-term prediction. In this paper, we investigate the problem of selecting noncontiguous input v…
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Mutual information and gamma test for input selection
by Nima Reyhani, Jin Hao, Yongnan Ji, Amaury Lendasse — 2005 — in: Proceedings of European Symposium on Artificial Neural Networks, ESANN 2005
…Abstract. In this paper, input selection is performed using two different approaches. The first approach is based on the Gamma test. This test estimates the mean square error (MSE) that can be achieved without overfitting. The best set of inputs is the one that minimises the result of the Gamma test…
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Pruned Lazy Learning Models for Time Series Prediction
by Antti Sorjamaa, Amaury Lendasse, Michel Verleysen — 2005 — In Proceedings of ESANN 2005
…Abstract. This paper presents two improvements of Lazy Learning. Both methods include input selection and are applied to long-term prediction of time series. First method is based on an iterative pruning of the inputs and the second one is performing a brute force search in the possible set of input…
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Analysis of fast input selection: Application in time series prediction
by Jarkko Tikka, Amaury Lendasse, Jaakko Hollmén — 2006 — ICANN 2006. LNCS
…Abstract. In time series prediction, accuracy of predictions is often the primary goal. At the same time, however, it would be very desirable if we could give interpretation to the system under study. For this goal, we have devised a fast input selection algorithm to choose a parsimonious, or sparse…
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Forecasting Time-Series by Kohonen
by Classification Amaury Lendasse, Amaury Lendasse, Michel Verleysen, Eric De Bodt, Marie Cottrell — 1998 — in Proc. of ESANN’98
…In this paper, we propose a generic non-linear approach for time series forecasting. The main feature of this approach is the use of a simple statistical forecasting in small regions of an input space adequately chosen and quantized. The partition of the space is achieved by the Kohonen algorithm…
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Using the Delta Test for Variable Selection
by Emil Eirola, Elia Liitiäinen, Amaury Lendasse, Francesco Corona, Michel Verleysen
…Abstract. Input selection is an important consideration in all large-scale modelling problems. We propose that using an established noise variance estimator known as the Delta test as the target to minimise can provide an effective input selection methodology. Theoretical justifications and experime…
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