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Advanced Statistical Methods for the Analysis of Large by Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez PDF

By Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez

ISBN-10: 3642210368

ISBN-13: 9783642210365

The subject of the assembly was once “Statistical tools for the research of huge Data-Sets”. in recent times there was expanding curiosity during this topic; in reality an immense volume of knowledge is frequently on hand yet usual statistical strategies aren't compatible to handling this type of facts. The convention serves as a big assembly aspect for eu researchers engaged on this subject and a few ecu statistical societies participated within the association of the development.   The publication contains forty five papers from a range of the 156 papers authorised for presentation and mentioned on the convention on “Advanced Statistical tools for the research of huge Data-sets.”

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Additional info for Advanced Statistical Methods for the Analysis of Large Data-Sets (Studies in Theoretical and Applied Statistics / Selected Papers of the Statistical Societies)

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From a theoretical point of view, the problem of jointly aligning and clustering curves is soundly posed when the number of cluster k, the similarity index between two functions, and the set W of admissible warping functions are chosen. Let us mention two special choices that make the k-mean alignment algorithm degenerate to the continuous alignment algorithm and to the k-mean clustering algorithm, respectively: k D 1 and W D f1g. 3 Analysis of Internal Carotid Artery Centerlines In this section we discuss a real application of the k-mean alignment procedure that is also the one that urged us to develop such method: the analysis of the AneuRisk dataset.

2001). yj ; yj 0 /: (6) j;j 0 Points, whose distance from the surface of the hypersphere is less than , are the support vectors and they define a partition of the feature space. These points are characterized by 0 < ˇi < C ; points with ˇi D C are called bounded support vectors and they are outside the feature-space hypersphere. If ˇi D 0 the point is inside the feature-space hypersphere. The number of support vectors affects the number of clusters, as the number of support vectors increases the number of clusters increases.

Muller K. , Scholkopf B. (2001) An introduction to Kernel-based learning algorithms, IEEE transiction on neural networks, 12: 181–201. Saporta G. (1990) Simultaneous analysis of qualitative and quantitative data, Atti 35ı Riunione Scientifica della Societ`a italiana di Statistica, CEDAM: 63–72. , Cristianini N. (2004) Kernel methods for pattern analysis, Cambridge University Press. Boston. , Tuzhilin A. (1996) What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering Vol 8: 291–304.

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Advanced Statistical Methods for the Analysis of Large Data-Sets (Studies in Theoretical and Applied Statistics / Selected Papers of the Statistical Societies) by Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez


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