Abstract
Data analysis, in its various forms and manifestations, plays a role in many of the practical problems addressed at the Fraunhofer ITWM. This chapter offers insights into typical data analysis questions and the procedures we use to find appropriate answers. Our focus here is on the relationship to the application and the presentation of those methods that have proven themselves effective in our work, rather than on providing a complete methodological overview.
We first discuss aspects that directly concern the data, such as data source, data quality, information richness, data integration, and data pre-processing. Then, we turn our attention to methods of data-based modeling from the fields of data mining and machine learning. We examine these methods from the perspective of statistical learning theory, returning in each instance to their relationship to the practical problems under consideration.
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References
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, Berlin (2008)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)
Györfy, L., Kohler, M., Krzyźak, A., Walk, H.: A Distribution-Free Theory of Nonparametric Regression. Springer, Berlin (2002)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, Data Mining, Inference, and Prediction. 2nd edn. Springer, Berlin (2008)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, New York (1999)
Härdle, W.: Applied Nonparametric Regression. Cambridge University Press, Cambridge (1990)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)
Knaf, H.: Distanzen zwischen Partitionen – zur Anwendung und Theorie. Technical Report 226, Fraunhofer ITWM, Kaiserslautern (2013)
Lee, T.W.: Independent Component Analysis: Theory and Applications. Kluwer Academic, Norwell (2000)
Linton, O., Härdle, W.: Nonparametric regression. In: Banks, D., Kotz, S. (eds.) Encyclopedia of Statistical Science, vol. X. Wiley, New York (1998)
Meila, M.: Comparing clusterings – an axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning, vol. 84, pp. 1003–1013 (2005)
Montavon, G., Orr, G., Müller, K.R.: Neural Networks: Tricks of the Trade. 2nd edn. Lecture Notes in Computer Science, vol. 7700. Springer, Berlin (2012)
Silverman, B.: Spline smoothing: the equivalent variable kernel method. Ann. Stat. 12, 898–916 (1984)
Vapnik, V.N.: Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing, Communications and Control. Wiley, New York (1998)
White, H.: Some asymptotic results for learning in single hidden-layer feedforward network models. J. Am. Stat. Assoc. 84, 1003–1013 (1989)
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Lang, P., Franke, J. (2015). Data Analysis. In: Neunzert, H., Prätzel-Wolters, D. (eds) Currents in Industrial Mathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48258-2_4
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DOI: https://doi.org/10.1007/978-3-662-48258-2_4
Publisher Name: Springer, Berlin, Heidelberg
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