Dear Matthieu, I don't know very well scikit. The Svm is implemented by Sequential Minimal Optimization (SMO). As for Terminated Ramps (TR) you can read this paper: /S. Merler and G. Jurman/* Terminated Ramp - Support Vector Machine: a nonparametric data dependent kernel* Neural Network, 19(10), 1597-1611, 2006. /* da */ Matthieu Brucher ha scritto:
Hi,
How does it compare to the elarn scikit, especially for the SVM part ? How was it implemented ?
Matthieu
2008/2/14, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu>>:
*Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes:
* *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions
Requires Python <http://www.python.org/> >= 2.4 and NumPy <http://www.scipy.org/> >= 1.0.3.* MLPY* is a project of MPBA Group <http://mpa.fbk.eu/> (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). <http://www.fbk.eu/>* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 <http://www.gnu.org/licenses/gpl-3.0.html>.
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