Abstract
We present a package for R language containing a set of tools for regression using ensembles of learning machines and for time series forecasting. The package contains implementations of Bagging and Adaboost for regression, and algorithms for computing mutual information, autocorrelation and false nearest neighbors.
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References
Abarbanel, H.D.I. Analysis of Observed Chaotic Data. Springer, New York, 1996.
Becker, R.A. and Chambers, J.M. Design of the S System for Data Analysis. Comm. A.C.M., 27:5 pp. 486–495, 1984.
Breiman, L. Bagging predictors. Machine Learning, 24:123–140, 1996.
Chang, C.C. and Lin, C.J. Training ν-support vector classifiers: Theory and algorithms. Neural Computation, 13(9):2119–2147, 2001.
Chang, C.C. and Lin, C.J. Training ν-support vector regression: Theory and algorithms. Neural Computation, 14(8):1959–1977, 2002.
Cortes, C. and Vapnik, V. Support Vector Networks. Machine Learning, 20:273–297, 1995.
Domingos, P. A Unified Bias-Variance Decomposition for Zero-One and Squared Loss In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pages 564–569, Austin, TX, AAAI Press, 2000.
Drucker, H. Improving regressors using boosting techniques. In D. H. Fisher, editor, Proceedings of the Fourteenth International Conference on Machine Learning, pages 107–115. Morgan Kaufmann, 1997.
Freund, Y. and Schapire, R.E. Experiments with a New Boosting Algorithm. Proceedings of the Thirteenth Conference, ed: L. Saitta, Morgan Kaufmann, pp. 148–156, 1996.
Geman, S., Bienenstock, E. and Doursat, R. Neural networks and the bias-variance dilemma. Neural Computation, 4(1):1–58, 1992.
Ihaka, R. and Gentleman, R. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3):299–314, 1996.
Mane, R. On the dimension of the compact invariant sets of certain non-linear maps. In D.A Rand and L. S. Young, editors, Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898 p. 230–242, 1981. Springer-Verlag, Berlin.
Nene, S.A. and Nayar, S.K. A simple algorithm for nearest neighbor search in high dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:989–1003, 1997.
Ripley, B.D. Pattern Recognition and Neural Networks. Cambridge, 1996.
Takens, F. Detecting strange attractors in turbulence. In D.A. Rand and L.-S. Young, editors, Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898, pp. 366–381, 1981. Springer-Verlag, Berlin.
Vautard, R. and Ghil, M. Singular-spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D, 35:395–424, 1989.
Vautard, R., You, P., and Ghil, M. Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D, 58:95–126, 1992.
Valentini, G. and Masulli, F. Ensembles of Learning Machines, in M. Marinaro and R. Tagliaferri, editors, Neural Nets WIRN Vietri-02, Series Lecture Notes in Computer Sciences, Springer-Verlag, Heidelberg (Germany), vol. 2486, pp.3–19, 2002
Valentini, G. and Dietterich, T.G. Bias-variance analysis of Support Vector Machines for the development of SVM-based ensemble methods Journal of Machine Learning Research (in press).
Vapnik, V.N. Statistical Learning Theory. Wiley, New York, 1998.
Venables, W.N. and Ripley, B.D. Modern Applied Statistics with S. Springer, 2002.
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Filippone, M., Masulli, F., Rovetta, S. (2005). ERAF: A R Package for Regression and Forecasting. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_20
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DOI: https://doi.org/10.1007/1-4020-3432-6_20
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
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