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Titlebook: Data Analysis; Scientific Modeling Wolfgang Gaul,Otto Opitz,Martin Schader Book 2000 Springer-Verlag Berlin · Heidelberg 2000 Optimization

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Advances in Alzheimer Disease Therapyves an accurate estimation of the density probabity function of the data, an adapted hierarchical clustering allows to take into account an extra knowledge given by an expert. We present two actual applications of the method taken in the domain of geophysics.
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Radiation Therapy for Chondrosarcomamixtures of multivariate normals where the expected value for each component depends on possibly non-normal regressor variables. The expected values and covariance matrices of the mixture components are parameterized using conditional mean- and covariance-structures. We discuss the construction of t
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Erik J. Geiger,Nicholas M. Bernthalsensus dissimilarities out of a . of dissimilarities is NP-hard for ultrametrics, quasi-utrametrics and proper dissimilarities satisfying the Bertrand and Janowitz k-point inequality. The NP-hardness of finding a consensus dissimilarity for a pyramid (also called an indexed pseudo-hierarchy) is also
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Systemic Therapy for Chondrosarcomaality of a given multivariate data set using an unsupervised mixture of cubic B-Spline in density estimation in one dimensional spece. In the multivariate case, this is achieved by utilizing the Mahalanobis (1936) distance of each point from the multivariate mean (centroid), Mahalanobis distance dat
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Muhammad Omar Hakim,Andrew Eric Rosenbergs are the class of representations concerned, the criterion function employed, and the transformations of data and/or representation that are either required or permitted. A variety of benefits are noted. In particular, this framework helps to establish the state-of-the-art and, thereby, guides futu
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https://doi.org/10.1057/9780230375758ively agglomerates pairs of leaves to form larger and larger clusters, while the latter proceeds by stepwise addition of objects to a growing tree. A third approach involves improving the global fitness of an initial tree by exchanging subtrees. This article suggests that the shape of inferred trees
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