Titlebook: Analysis of Large and Complex Data; Adalbert F.X. Wilhelm,Hans A. Kestler Conference proceedings 2016 Springer International Publishing Sw

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Pointwise Dimension for Hyperbolic Dynamicsd in terms of direct inducing of a hierarchy through use of the Baire metric; and (2) based on clusters found, selecting subsets of the original data for further analysis. In this work, we focus on random projectionthat is used for processing high dimensional data. A random projection, outputting a

Postulate

Quantitative Recurrence and Dimension Theoryses of criteria; we focus on those penalizing the log-likelihoodwith a penalty term, that accounts for model complexity. However, a full likelihood is not always computationally feasible. To overcome this issue, the likelihood is replaced with a surrogateobjective function. Thus, a question arises n

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General Concept of Multifractal Analysison-based. In this paper, we propose a method guided by the quadtreedecomposition. The principle of the method is to recursively decompose regions of a document image is four equal regions, starting from the image of the whole document. At each step of the decomposition process an OCR engine is used

Palter

Pointwise Dimension for Hyperbolic Dynamics low-dimensional decision rules with interpretable signatures are of great importance. Feature selection processes are essential for fulfilling these design criteria. They select small subsets of highly informative features that can be starting points for new biological hypotheses and experiments. I

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Multidimensional Spectra and Number TheoryFor the many featurescase we look at projection methods, distance-based methods, and feature selection. For the many observationscase we mainly consider subsampling. The examples in this paper show that standard classificationmethods should not be blindly applied to Big Data.

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