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WEKA software using the libSVM library was used to build the SVM classifier.
Support Vector Machines: Support Vector Machine implementation using the LibSVM library [23].... two workers do only support regression... Multiple Linear Regression: Multiple linear regression algorithm.
All training and testing procedures were carried out with Matlab software (Matlab, Natick, USA), using the libSVM library [13] for defining the SVM classifiers.
Feature detectors for the paws, snout and tail were trained using the LIBSVM library.
SVM regression was performed using the LibSVM library (Chang and Lin 2011).
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It this work, this parameter has been kept constant using the LibSVM 2.83 [59] default settings.
We performed SVM classification using the LibSVM software [ 41].
> -wrap-foot> Two support vector machines (SVMs) were trained with linear kernel functions, using the libsvm package (http://www.csie.ntu.edu.tw/~cjlin/libsvm).ntu.edu.tw/~cjlin/libsvm
The classification was performed with a SVM using the LIBSVM (http://www.csie.ntu.edu.tw/∼cjlin/libsvm/) implementation.
The method was implemented in Python using the LIBSVM package (http://www.csie.ntu.edu.tw/~cjlin/libsvm).ntu.edu.tw/~cjlin/libsvm
Subsequently, BOLD activation patterns were analyzed using the LIBSVM-based RFE.
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