SVM Class Reference
[Machine Learning]

Predict options.

Enumerator:

RAW_OUTPUT

makes the method return the raw results (the sum), not the class label

Reimplemented in DTrees.

Definition at line 296 of file ml.hpp.

SVM kernel type

A comparison of different kernels on the following 2D test case with four classes. Four SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score. Bright means max-score > 0, dark means max-score < 0. 

Enumerator:

CUSTOM	Returned by SVM::getKernelType in case when custom kernel has been set.
LINEAR	Linear kernel. No mapping is done, linear discrimination (or regression) is done in the original feature space. It is the fastest option. .
POLY	Polynomial kernel: .
RBF	Radial basis function (RBF), a good choice in most cases. .
SIGMOID	Sigmoid kernel: .
CHI2	Exponential Chi2 kernel, similar to the RBF kernel: .
INTER	Histogram intersection kernel. A fast kernel. .

Definition at line 602 of file ml.hpp.

SVM params type

Definition at line 624 of file ml.hpp.

SVM type

Enumerator:

C_SVC	C-Support Vector Classification. n-class classification (n 2), allows imperfect separation of classes with penalty multiplier C for outliers.
NU_SVC	-Support Vector Classification. n-class classification with possible imperfect separation. Parameter (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C.
ONE_CLASS	Distribution Estimation (One-class SVM). All the training data are from the same class, SVM builds a boundary that separates the class from the rest of the feature space.
EPS_SVR	-Support Vector Regression. The distance between feature vectors from the training set and the fitting hyper-plane must be less than p. For outliers the penalty multiplier C is used.
NU_SVR	-Support Vector Regression. is used instead of p. See LibSVM for details.

Definition at line 573 of file ml.hpp.

Computes error on the training or test dataset.

Parameters:

data	the training data
test	if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
resp	the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0-100%).

Clears the algorithm state.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2984 of file core.hpp.

Creates empty model.

Use StatModel::train to train the model. Since SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::trainAuto.

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented from Algorithm.

Parameter _C_ of a SVM optimization problem.

For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

See also:

setC

Optional weights in the SVM::C_SVC problem, assigned to particular classes.

They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus these weights affect the misclassification penalty for different classes. The larger weight, the larger penalty on misclassification of data from the corresponding class. Default value is empty Mat.

See also:

setClassWeights

Parameter _coef0_ of a kernel function.

For SVM::POLY or SVM::SIGMOID. Default value is 0.

See also:

setCoef0

Retrieves the decision function.

Parameters:

i	the index of the decision function. If the problem solved is regression, 1-class or 2-class classification, then there will be just one decision function and the index should always be 0. Otherwise, in the case of N-class classification, there will be decision functions.
alpha	the optional output vector for weights, corresponding to different support vectors. In the case of linear SVM all the alpha's will be 1's.
svidx	the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear SVM each decision function consists of a single "compressed" support vector.

The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.

Generates a grid for SVM parameters.

Parameters:

param_id

SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is 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 SVM::trainAuto.

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Parameter _degree_ of a kernel function.

For SVM::POLY. Default value is 0.

See also:

setDegree

Parameter of a kernel function.

For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

See also:

setGamma

Type of a SVM kernel.

See SVM::KernelTypes. Default value is SVM::RBF.

Parameter of a SVM optimization problem.

For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

See also:

setNu

Parameter of a SVM optimization problem.

For SVM::EPS_SVR. Default value is 0.

See also:

setP

Retrieves all the support vectors.

The method returns all the support vectors as a floating-point matrix, where support vectors are stored as matrix rows.

Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem.

You can specify tolerance and/or the maximum number of iterations. Default value is `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`;

See also:

setTermCriteria

Type of a SVM formulation.

See SVM::Types. Default value is SVM::C_SVC.

See also:

setType

Retrieves all the uncompressed support vectors of a linear SVM.

The method returns all the uncompressed support vectors of a linear SVM that the compressed support vector, used for prediction, was derived from. They are returned in a floating-point matrix, where the support vectors are stored as matrix rows.

Returns the number of variables in training samples.

Returns true if the model is classifier.

Returns true if the model is trained.

Loads algorithm from the file.

Parameters:

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

Definition at line 3027 of file core.hpp.

Loads algorithm from a String.

Parameters:

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

Definition at line 3046 of file core.hpp.

Predicts response(s) for the provided sample(s)

Parameters:

samples	The input samples, floating-point matrix
results	The optional output matrix of results.
flags	The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Implemented in LogisticRegression.

Reads algorithm parameters from a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2992 of file core.hpp.

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::read<SVM>(fn);

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3008 of file core.hpp.

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Parameter _C_ of a SVM optimization problem.

See also:

getC

Optional weights in the SVM::C_SVC problem, assigned to particular classes.

See also:

getClassWeights

Parameter _coef0_ of a kernel function.

See also:

getCoef0

Initialize with custom kernel.

See SVM::Kernel class for implementation details

Parameter _degree_ of a kernel function.

See also:

getDegree

Parameter of a kernel function.

See also:

getGamma

Initialize with one of predefined kernels.

See SVM::KernelTypes.

Parameter of a SVM optimization problem.

See also:

getNu

Parameter of a SVM optimization problem.

See also:

getP

Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem.

See also:

getTermCriteria

Type of a SVM formulation.

See also:

getType

Create and train model with default parameters.

The class must implement static `create()` method with no parameters or with all default parameter values

Definition at line 357 of file ml.hpp.

Trains the statistical model.

Parameters:

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

Trains the statistical model.

Parameters:

trainData	training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flags	optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Trains an SVM with optimal parameters.

Parameters:

data	the training data that can be constructed using TrainData::create or TrainData::loadFromCSV.
kFold	Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the SVM algorithm is executed kFold times.
Cgrid	grid for C
gammaGrid	grid for gamma
pGrid	grid for p
nuGrid	grid for nu
coeffGrid	grid for coeff
degreeGrid	grid for degree
balanced	If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset.

The method trains the SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal.

If there is no need to optimize a parameter, the corresponding grid step should be set to any value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value `Gamma` is taken for gamma.

And, finally, if the optimization in a parameter is required but the corresponding grid is unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for gamma, call `SVMgetDefaultGrid(SVM::GAMMA)`.

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

Stores algorithm parameters in a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2988 of file core.hpp.