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// Copyright (C) 2002 Ronan Collobert (collober@iro.umontreal.ca)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// Torch is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef SVM_REGRESSION_INC
#define SVM_REGRESSION_INC
#include "SVM.h"
namespace Torch {
/** SVM in regression.
Try to find the hyperplane f(x) = w.x+b
as
$(w,b)$ minimize $0.5*||w||^2 + \sum_j C_j |w.x_j+b -y_j -eps|_+$
$+ \sum_j C_(j+n) |y_j -w.x_j-b -eps|_+$
(where $|x|_+ = x$ if $x > 0$, else $0$)
(and $n$ is the number of training examples)
(the size of $C$ is here 2*$n$)
("eps" is #eps_regression# in the code)
(in fact, we use a kernel #kernel# instead of
a dot product)
The $C_j$ coefficients are given by #C_# when you
call the constructor. If this one is NULL, all
#C_j# will have the value given by the "C" option.
(The size of #C_# \emph{must be} #2*data->n_real_examples#)
Options:
\begin{tabular}{lcll}
"eps regression" & real & size of the error tube & [0.7]
\end{tabular}
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class SVMRegression : public SVM
{
public:
bool C_is_allocated;
real eps_regression;
real *Cuser;
//-----
///
SVMRegression(Kernel *kernel_, real *C_=NULL);
//-----
virtual void checkSupportVectors();
virtual void reset();
virtual ~SVMRegression();
};
}
#endif
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