Участник:Tatarchuk
Материал из MachineLearning.
(Различия между версиями)
(→Selective Support Vector Machines) |
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=== Overview === | === Overview === | ||
| - | SelSVMs | + | 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: | The main features of the algorithms are the following: | ||
* work on small training sets with large number of features/kernels; | * work on small training sets with large number of features/kernels; | ||
| - | * provide feature selection with controlled selectivity level | + | * provide feature selection with controlled selectivity level; |
* use Matlab optimization solver and Mosek solver; | * use Matlab optimization solver and Mosek solver; | ||
* support precompiled kernels; | * support precompiled kernels; | ||
* return a posteriori probability estimates; | * return a posteriori probability estimates; | ||
| + | |||
=== How to use === | === How to use === | ||
| + | |||
=== Matlab code === | === Matlab code === | ||
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=== Publications === | === Publications === | ||
Версия 12:13, 14 октября 2008
Татарчук Александр Игоревич
Содержание |
Selective Support Vector Machines
Description
Authors V. Mottl (vmottl@ya.ru) A. Tatarchuk (aitech@ya.ru) A. Eliseev (andreyel@gmail.com)
Developed at Computing Center of 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;
