Mthrun

ABCanalysis

ABC Analysis computes optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few").

AdaptGauss

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes.

Classifiers

Common Supervised Maschine Learning algorithms

DatabionicSwarm

The Databionic swarm is an unsupervised machine learning method for cluster analysis and the visualization of structures of high-dimensional data.

DataVisualizations

A collection of various visualizations methods is provided. The flagship is explorative data science using distribution analysis and PDE-optimized violin plots.

Distances

Functions For Different Distance Measures

FCPS

The Fundamental Clustering Problems Suite (FCPS) summaries 54 state-of-the-art clustering algorithms, common cluster challenges and estimations of the number of clusters as well as the testing for cluster tendency.

ForecastingElectricityPrice2019

Lecture and materials of a talk given at the Kiel University of Applied Sciences

ProjectionBasedClustering

A clustering approach for every projection method based on the generalized U*-matrix visualization of a topographic map

TSAT

Time Series Analysis Tools (TSAT) can be used to describe event-pattern detection as a part of Complex event processing (CEP) for categorial time series and gives several approaches for numerical times series, like Filtering through FFT, WVT or predictions (e.g. compound model).

Mthrun/FCPS

The Fundamental Clustering Problems Suite (FCPS) summaries 54 state-of-the-art clustering algorithms, common cluster challenges and estimations of the number of clusters as well as the testing for cluster tendency.

Mthrun/DatabionicSwarm

The Databionic swarm is an unsupervised machine learning method for cluster analysis and the visualization of structures of high-dimensional data.

Mthrun/DataVisualizations

A collection of various visualizations methods is provided. The flagship is explorative data science using distribution analysis and PDE-optimized violin plots.

Mthrun/ProjectionBasedClustering

A clustering approach for every projection method based on the generalized U*-matrix visualization of a topographic map

Mthrun/TSAT

Time Series Analysis Tools (TSAT) can be used to describe event-pattern detection as a part of Complex event processing (CEP) for categorial time series and gives several approaches for numerical times series, like Filtering through FFT, WVT or predictions (e.g. compound model).

Mthrun/ABCanalysis

ABC Analysis computes optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few").

Mthrun/AdaptGauss

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes.

Mthrun/Classifiers

Common Supervised Maschine Learning algorithms

Mthrun/Distances

Functions For Different Distance Measures

Mthrun/ForecastingElectricityPrice2019

Lecture and materials of a talk given at the Kiel University of Applied Sciences

Mthrun/GeneralizedUmatrix

Credible Visualization for Two-Dimensional Projections of Data

Mthrun/RHmm

Hidden Markov Models

Mthrun/AdaptGauss2D

Interactive Gaussian Mixture Modeling in 2D

Mthrun/assignment-2

Mthrun/BIDistances

Bioinformatic Distances

Mthrun/Corona2020

Open source code for the extended abstract of the journal track of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020)

Mthrun/dbt.DataIO

IO Handling for Files Containing Data

Mthrun/dbt.FlowCytometry

Databionics Toolbox for the Analysis of Fluorescence-Activated Cell Sorting (FACS) Data

Mthrun/Deeplearning4ObjectDetection

Vergleich der Deep-Learning-Ansätze “YOLO” und “R-CNN” für den Bereich der bildbasierten Objekterkennung

Mthrun/DRquality

Several quality measurements for investigating the performance of dimensionality reduction methods are provided here. In addition a new quality measurement called Gabriel classification error is made accessible.

Mthrun/ExplainableAI4TimeSeries2020

An explainable AI (XAI) frame based on swarm intelligence is introduced and applied on the use case of multivariate time series in order to explain states of water bodies.

Mthrun/GraphAlgorithms

Neighborhood Graphs for Lectures of Knowledge Discovery

Mthrun/ImageProcessing

ImageProcessing package for the lecture about deep learning for Object detection

Mthrun/meetings

Code snippets from the meetings of the Marburg R User Group

Mthrun/ModelFittingData2PDF2021

Vortrag und Unterlagen

Mthrun/mthrun.github.io

Mthrun/PDEbayes

Nonparametric naiv bayes classifier using pareto density estimation as well as a parametric naiv bayes classifier using robustly estimated mean and standard deviation.

Mthrun/pickit-docs

Sources for docs.pickit3d.com

Mthrun/RLwithAppl_IntAgents

Reinforcement Learning with Applications of Intelligent Agents

Mthrun/ScatterDensity

The tool allows the user to perform two variants of two-dimensional density estimation, namely SDH and PDE.