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:

New: fss: a feature ranking algorithm is included. cat2bin: converts categorial data into binary data Improvements: train_sc supports psvm with weighted samples, PLA and Winnow algorithm

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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