Участник:Tatarchuk
Материал из MachineLearning.
(Различия между версиями)
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* return a posteriori probability estimates; | * return a posteriori probability estimates; | ||
| + | There are four algorithms in SelSVM library: | ||
| + | |||
| + | * Selective Relevance Feature Machine (SelRFM) - | ||
| + | * Selective Relevance Feature Machine (SelRKM) - | ||
| + | * Selective Support Feature Machine (SelSFM) - | ||
| + | * Selective Support Kernel Machine (SelSKM) - | ||
=== How to use === | === How to use === | ||
Версия 12:49, 14 октября 2008
Татарчук Александр Игоревич
Содержание |
Selective Support Vector Machines
Description
Authors V. Mottl A. Tatarchuk A. Eliseev
Developed at Computing Center of Russian Academy of Sceince, Moscow, Russia
Version 1.0
Date 14.10.2008
Overview
SelSVMs are modifications of classical Support Vector Machine provided with controlled selectivity property of feature/kernel selection.
The main features of the algorithms are the following:
- work on small training sets with large number of features/kernels;
- provide feature selection with controlled selectivity level;
- use Matlab optimization solver and Mosek solver;
- support precompiled kernels;
- return a posteriori probability estimates;
There are four algorithms in SelSVM library:
- Selective Relevance Feature Machine (SelRFM) -
- Selective Relevance Feature Machine (SelRKM) -
- Selective Support Feature Machine (SelSFM) -
- Selective Support Kernel Machine (SelSKM) -
