Support Vector Machines

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.


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Support Vector Machines.

class CvSVM : public CvStatModel
    // 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 };

    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; }



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SVM training parameters.

struct 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 CvSVM .


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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 CvSVMParams structure.


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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.

  • 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 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 ( min , min*step , min*{step}^2 , ... min*{step}^n ) where min is gamma_grid.min_val , step is gamma_grid.step , and n is the maximal index such, that

\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.


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CvParamGrid CvSVM::get_default_grid(int param_id)

Generates a grid for the SVM parameters.

  • param_id

    Must be one of the following:

    • CvSVM::C
    • CvSVM::GAMMA
    • CvSVM::P
    • CvSVM::NU
    • CvSVM::COEF


    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 .


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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 .


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int CvSVM::get_support_vector_count() const
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.

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