Description:

The NaN-toolbox provides a number of statistics functions and machine learning methods for the use with Octave and Matlab. The functions can handle data with missing values encoded as NaNs, weighting of data samples, and multi-class classification (using a one-versus-rest scheme). There is a common interface to a number of different classification methods (including FDA, LDA, Naive Bayes, QDA, RDA, sparse classifiers, interfaces to some SVMs, regression/PLS, Wiener-Hopf).

Changes to previous version:

fixed STD and VAR: fixed empty opt did not resolve to default removed REM and MOD in order to avoid annoying warnings

Some minor bug fixes. For details see: http://hci.tugraz.at/schloegl/matlab/NaN/CHANGELOG

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Matlab

Tags: Classification, Multi Class, Machine Learning, Missing Data, Statistics, Weighting

Archive: download here

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