Fractal: Long range dependence, Self-similarity, Multifractality
Theory, methods and applications
This tutorial illustrates the basic elements of the MF_bs_tool toolbox, through the analysis of a corrupted self-similar process.
It shows how to set the basic properties of the tool, how to select the scaling range, and how to obtain bootstrap-based confidence intervals.
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This tutorial illustrates a more advanced use of the MF_BS_tool toolbox, through the analysis of a corrupted multifractal process.
It shows how to select the scaling range, fractional integration order and range of statistical orders
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This tutorial shows how to use MF_bs_tool for the analysis of an entire database of real-world data. As an example, we will analyze part of the Normal Sinus Rhythm RR Interval Database, made available by the Physionet project.
The demo shows how to perform the analysis of all signals in the database in a single command.
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This tutorial shows how to use a Sparse Support Vector Machine for joint classification and feature selection. It proposes a simple classification task of data coming from a mixture of Gaussian distributions with correlated and random features. Performance is compared to that of a classical SVM.
The tutorial also shows how to perform cross-validation for performance evaluation in a simple way.
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