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# Support Vector Data Description (SVDD)
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[![View Support Vector Data Description (SVDD) on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://ww2.mathworks.cn/matlabcentral/fileexchange/69296-support-vector-data-description-svdd)
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MATLAB Code for abnormal detection or fault detection using SVDD.
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Version 2.0, 1-DEC-2019
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* SVDD model for training dataset containing only positive training data. (SVDD)
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* SVDD model for training dataset containing both positive training data and negative training data. (nSVDD)
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* Multiple kinds of kernel functions.
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* Visualization module including ROC curve plotting, testing result plottong, and decision boundary.
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* Visualization module including ROC curve plotting, testing result plotting, and decision boundary.
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* Dimensionality Reduction (DR) module based on ['drtoolbox'](http://lvdmaaten.github.io/drtoolbox).
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* Parameter Optimization (PO) module based on the ['YPEA' toolbox](https://yarpiz.com/477/ypea-yarpiz-evolutionary-algorithms?tdsourcetag=s_pctim_aiomsg).
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* Preprocessing module for data standardization or data normalization.
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## About SVDD model
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Two types of SVDD modles are built according to the following references:
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Two types of SVDD models are built according to the following references:
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[1] Tax D M J, Duin R P W. Support vector data description[J]. Machine learning, 2004, 54(1): 45-66.
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## About Dimensionality Reduction (DR) module
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Dimensionality Reduction module is realized based on 'drtoolbox', contains Matlab implementations of 34 techniques for dimensionality reduction and metric learning.
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Dimensionality Reduction module is realized based on 'drtoolbox', which contains Matlab implementations of 34 techniques for dimensionality reduction and metric learning.
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For details about 'drtoolbox', please visit the website: http://lvdmaaten.github.io/drtoolbox
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## About Parameter Optimization (PO) module
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Parameter Optimization module is realized based on on the YPEA toolbox, contains Matlab implementations of 14 techniques for Parameter Optimization and metric learning.
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Parameter Optimization module is realized based on the YPEA toolbox, which contains Matlab implementations of 14 techniques for Parameter Optimization and metric learning.
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For details about YPEA toolbox, please visit the website: https://yarpiz.com/477/ypea-yarpiz-evolutionary-algorithms?tdsourcetag=s_pctim_aiomsg
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