opencv/modules/ml/doc/support_vector_machines.rst

Support Vector Machines
=======================

.. highlight:: cpp

Originally, support vector machines (SVM) was a technique for building an optimal (in some sense) binary (2-class) classifier. Then the technique has been extended to regression and clustering problems. SVM is a partial case of kernel-based methods, it maps feature vectors into higher-dimensional space using some kernel function, and then it builds an optimal linear discriminating function in this space (or an optimal hyper-plane that fits into the training data, ...). in the case of SVM the kernel is not defined explicitly. Instead, a distance between any 2 points in the hyper-space needs to be defined.

The solution is optimal in a sense that the margin between the separating hyper-plane and the nearest feature vectors from the both classes (in the case of 2-class classifier) is maximal. The feature vectors that are the closest to the hyper-plane are called "support vectors", meaning that the position of other vectors does not affect the hyper-plane (the decision function).

There are a lot of good references on SVM. Here are only a few ones to start with.

*
    **[Burges98] C. Burges. "A tutorial on support vector machines for pattern recognition", Knowledge Discovery and Data Mining 2(2), 1998.**
    (available online at
    http://citeseer.ist.psu.edu/burges98tutorial.html
    ).

*
    **LIBSVM - A Library for Support Vector Machines. By Chih-Chung Chang and Chih-Jen Lin**
    (
    http://www.csie.ntu.edu.tw/~cjlin/libsvm/
    )

.. index:: CvSVM

.. _CvSVM:

CvSVM
-----
.. c:type:: CvSVM

Support Vector Machines. ::

    class CvSVM : public CvStatModel
    {
    public:
        // SVM type
        enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 };

        // SVM kernel type
        enum { LINEAR=0, POLY=1, RBF=2, SIGMOID=3 };

        // SVM params type
        enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };

        CvSVM();
        virtual ~CvSVM();

        CvSVM( const CvMat* _train_data, const CvMat* _responses,
               const CvMat* _var_idx=0, const CvMat* _sample_idx=0,
               CvSVMParams _params=CvSVMParams() );

        virtual bool train( const CvMat* _train_data, const CvMat* _responses,
                            const CvMat* _var_idx=0, const CvMat* _sample_idx=0,
                            CvSVMParams _params=CvSVMParams() );

        virtual bool train_auto( const CvMat* _train_data, const CvMat* _responses,
            const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams _params,
            int k_fold = 10,
            CvParamGrid C_grid      = get_default_grid(CvSVM::C),
            CvParamGrid gamma_grid  = get_default_grid(CvSVM::GAMMA),
            CvParamGrid p_grid      = get_default_grid(CvSVM::P),
            CvParamGrid nu_grid     = get_default_grid(CvSVM::NU),
            CvParamGrid coef_grid   = get_default_grid(CvSVM::COEF),
            CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) );

        virtual float predict( const CvMat* _sample ) const;
        virtual int get_support_vector_count() const;
        virtual const float* get_support_vector(int i) const;
        virtual CvSVMParams get_params() const { return params; };
        virtual void clear();

        static CvParamGrid get_default_grid( int param_id );

        virtual void save( const char* filename, const char* name=0 );
        virtual void load( const char* filename, const char* name=0 );

        virtual void write( CvFileStorage* storage, const char* name );
        virtual void read( CvFileStorage* storage, CvFileNode* node );
        int get_var_count() const { return var_idx ? var_idx->cols : var_all; }

    protected:
        ...
    };
..

.. index:: CvSVMParams

.. _CvSVMParams:

CvSVMParams
-----------
.. c:type:: CvSVMParams

SVM training parameters. ::

    struct CvSVMParams
    {
        CvSVMParams();
        CvSVMParams( int _svm_type, int _kernel_type,
                     double _degree, double _gamma, double _coef0,
                     double _C, double _nu, double _p,
                     CvMat* _class_weights, CvTermCriteria _term_crit );

        int         svm_type;
        int         kernel_type;
        double      degree; // for poly
        double      gamma;  // for poly/rbf/sigmoid
        double      coef0;  // for poly/sigmoid

        double      C;  // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
        double      nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
        double      p; // for CV_SVM_EPS_SVR
        CvMat*      class_weights; // for CV_SVM_C_SVC
        CvTermCriteria term_crit; // termination criteria
    };
..

The structure must be initialized and passed to the training method of
:ref:`CvSVM` .

.. index:: CvSVM::train

.. _CvSVM::train:

CvSVM::train
------------
.. c:function:: bool CvSVM::train(  const CvMat* _train_data,  const CvMat* _responses,                     const CvMat* _var_idx=0,  const CvMat* _sample_idx=0,                     CvSVMParams _params=CvSVMParams() )

    Trains SVM.

The method trains the SVM model. It follows the conventions of the generic ``train`` "method" with the following limitations: only the CV_ROW_SAMPLE data layout is supported, the input variables are all ordered, the output variables can be either categorical ( ``_params.svm_type=CvSVM::C_SVC`` or ``_params.svm_type=CvSVM::NU_SVC`` ), or ordered ( ``_params.svm_type=CvSVM::EPS_SVR`` or ``_params.svm_type=CvSVM::NU_SVR`` ), or not required at all ( ``_params.svm_type=CvSVM::ONE_CLASS`` ), missing measurements are not supported.

All the other parameters are gathered in
:ref:`CvSVMParams` structure.

.. index:: CvSVM::train_auto

.. _CvSVM::train_auto:

CvSVM::train_auto
-----------------
.. c:function:: train_auto(  const CvMat* _train_data,  const CvMat* _responses,          const CvMat* _var_idx,  const CvMat* _sample_idx,          CvSVMParams params,  int k_fold = 10,          CvParamGrid C_grid      = get_default_grid(CvSVM::C),          CvParamGrid gamma_grid  = get_default_grid(CvSVM::GAMMA),          CvParamGrid p_grid      = get_default_grid(CvSVM::P),          CvParamGrid nu_grid     = get_default_grid(CvSVM::NU),          CvParamGrid coef_grid   = get_default_grid(CvSVM::COEF),          CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) )

    Trains SVM with optimal parameters.

    :param k_fold: Cross-validation parameter. The training set is divided into  ``k_fold``  subsets, one subset being used to train the model, the others forming the test set. So, the SVM algorithm is executed  ``k_fold``  times.

The method trains the SVM model automatically by choosing the optimal
parameters ``C``,``gamma``,``p``,``nu``,``coef0``,``degree`` from
:ref:`CvSVMParams` . By optimal
one means that the cross-validation estimate of the test set error
is minimal. The parameters are iterated by a logarithmic grid, for
example, the parameter ``gamma`` takes the values in the set
(
:math:`min`,:math:`min*step`,:math:`min*{step}^2` , ...
:math:`min*{step}^n` )
where
:math:`min` is ``gamma_grid.min_val``,:math:`step` is ``gamma_grid.step`` , and
:math:`n` is the maximal index such, that

.. math::

    \texttt{gamma\_grid.min\_val} * \texttt{gamma\_grid.step} ^n <  \texttt{gamma\_grid.max\_val}

So ``step`` must always be greater than 1.

If there is no need in optimization in some parameter, the according grid step should be set to any value less or equal to 1. For example, to avoid optimization in ``gamma`` one should set ``gamma_grid.step = 0``,``gamma_grid.min_val``,``gamma_grid.max_val`` being arbitrary numbers. In this case, the value ``params.gamma`` will be taken for ``gamma`` .

And, finally, if the optimization in some parameter is required, but
there is no idea of the corresponding grid, one may call the function ``CvSVM::get_default_grid`` . In
order to generate a grid, say, for ``gamma`` , call ``CvSVM::get_default_grid(CvSVM::GAMMA)`` .

This function works for the case of classification
( ``params.svm_type=CvSVM::C_SVC`` or ``params.svm_type=CvSVM::NU_SVC`` )
as well as for the regression
( ``params.svm_type=CvSVM::EPS_SVR`` or ``params.svm_type=CvSVM::NU_SVR`` ). If ``params.svm_type=CvSVM::ONE_CLASS`` , no optimization is made and the usual SVM with specified in ``params`` parameters is executed.

.. index:: CvSVM::get_default_grid

.. _CvSVM::get_default_grid:

CvSVM::get_default_grid
-----------------------
.. c:function:: CvParamGrid CvSVM::get_default_grid( int param_id )

    Generates a grid for the SVM parameters.

    :param param_id: Must be one of the following:

            * **CvSVM::C**

            * **CvSVM::GAMMA**

            * **CvSVM::P**

            * **CvSVM::NU**

            * **CvSVM::COEF**

            * **CvSVM::DEGREE**
            .

        The grid will be generated for the parameter with this ID.

The function generates a grid for the specified parameter of the SVM algorithm. The grid may be passed to the function ``CvSVM::train_auto`` .

.. index:: CvSVM::get_params

.. _CvSVM::get_params:

CvSVM::get_params
-----------------
.. c:function:: CvSVMParams CvSVM::get_params() const

    Returns the current SVM parameters.

This function may be used to get the optimal parameters that were obtained while automatically training ``CvSVM::train_auto`` .

.. index:: CvSVM::get_support_vector*

.. _CvSVM::get_support_vector*:

CvSVM::get_support_vector*
--------------------------
.. c:function:: int CvSVM::get_support_vector_count() const

.. c:function:: const float* CvSVM::get_support_vector(int i) const

    Retrieves the number of support vectors and the particular vector.

The methods can be used to retrieve the set of support vectors.