ClassifyByDecisionBoundaries

The Decision Boundaries calculated through Bayes Theorem.

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. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.

Michael Thrun

AdaptGauss

Gaussian Mixture Models (GMM)

Onno Hansen-Goos

Rabea Griese

Catharina Lippmann

Florian Lerch

Quirin Stier

Jorn Lotsch

Luca Brinkmann

Alfred Ultsch

ClassifyByDecisionBoundaries function

<dl> <dt>Data</dt>
<dd>vector of Data</dd> <dt>DecisionBoundaries</dt>
<dd>decision boundaries, <code>BayesDecisionBoundaries</code></dd> <dt>ClassLabels</dt>
<dd>Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model</dd>
</dl>

Arguments

Author

Classify Data according to decision Boundaries — ClassifyByDecisionBoundaries

<dl>

 <dt>Data</dt>
<dd>vector of Data</dd>

 <dt>DecisionBoundaries</dt>
<dd>decision boundaries, <code>BayesDecisionBoundaries</code></dd>

 <dt>ClassLabels</dt>
<dd>Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model</dd>


</dl>

Classify Data according to decision Boundaries

ClassifyByDecisionBoundaries: Classify Data according to decision Boundaries

Description

Usage

Value

Arguments

Author

References

See Also