ml_SVMSGD

mlclassOpenCV 5.0.0

import { ml_SVMSGD } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

ARGUMENTSConstructor or factory

CLASSml_SVMSGD

RETURN TYPEOwned native handle

Call structure. A void return can still write to destination arguments. The parameter descriptions define inputs, outputs and ownership.

Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits ml_StatModel.

Stochastic Gradient Descent SVM Classifier * *************************************************************************************** Stochastic Gradient Descent SVM classifier

SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach, as presented in [bottou2010large].

The classifier has following parameters:

model type,
margin type,
margin regularization (\lambda),
initial step size (\gamma_0),
step decreasing power (c),
and termination criteria.

The model type may have one of the following values: SGD and ASGD.

SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula

w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}

where

w_t is the weights vector for decision function at step t,
\gamma(t) is the step size of model parameters at the iteration t, it is decreased on each step by the formula \gamma(t) = \gamma_0 (1 + \lambda \gamma_0 t) ^ {-c}
Q_i is the target functional from SVM task for sample with number i, this sample is chosen stochastically on each step of the algorithm.
ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula \widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}

The recommended model type is ASGD (following [bottou2010large]).

The margin type may have one of the following values: SOFT_MARGIN or HARD_MARGIN.

You should use HARD_MARGIN type, if you have linearly separable sets.
You should use SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers.
In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN.

The other parameters may be described as follows:

Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers (the less the parameter, the less probability that an outlier will be ignored). Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
Initial step size parameter is the initial value for the step size \gamma(t). You will have to find the best initial step for your problem.
Step decreasing power is the power parameter for \gamma(t) decreasing by the formula, mentioned above. Recommended value for SGD model is 1, for ASGD model is 0.75.
Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS. You will have to find the best termination criteria for your problem.

Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.

To use SVMSGD algorithm do as follows:

first, create the SVMSGD object. The algorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(), setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
then the SVM model can be trained using the train features and the correspondent labels by the method train().
after that, the label of a new feature vector can be predicted using the method predict().

// Create empty object
cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();

// Train the Stochastic Gradient Descent SVM
svmsgd->train(trainData);

// Predict labels for the new samples
svmsgd->predict(samples, responses);

ml_SVMSGD

Constructors and members