Trait opencv::hub_prelude::SVMSGDConst [−][src]
pub trait SVMSGDConst: StatModelConst {
fn as_raw_SVMSGD(&self) -> *const c_void;
fn get_svmsgd_type(&self) -> Result<i32> { ... }
fn get_margin_type(&self) -> Result<i32> { ... }
fn get_margin_regularization(&self) -> Result<f32> { ... }
fn get_initial_step_size(&self) -> Result<f32> { ... }
fn get_step_decreasing_power(&self) -> Result<f32> { ... }
fn get_term_criteria(&self) -> Result<TermCriteria> { ... }
}Expand description
! 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 (
),
- initial step size (
),
- step decreasing power (
),
- and termination criteria.
The model type may have one of the following values: \ref SGD and \ref ASGD.
-
\ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula
where
is the weights vector for decision function at step
,
is the step size of model parameters at the iteration
is the target functional from SVM task for sample with number
, this sample is chosen stochastically on each step of the algorithm.
-
\ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula
The recommended model type is ASGD (following bottou2010large).
The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN.
- You should use \ref HARD_MARGIN type, if you have linearly separable sets.
- You should use \ref 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
-
Step decreasing power is the power parameter for
-
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);Required methods
fn as_raw_SVMSGD(&self) -> *const c_void
Provided methods
fn get_svmsgd_type(&self) -> Result<i32>
fn get_svmsgd_type(&self) -> Result<i32>
fn get_margin_type(&self) -> Result<i32>
fn get_margin_type(&self) -> Result<i32>
fn get_margin_regularization(&self) -> Result<f32>
fn get_margin_regularization(&self) -> Result<f32>
fn get_initial_step_size(&self) -> Result<f32>
fn get_initial_step_size(&self) -> Result<f32>
fn get_step_decreasing_power(&self) -> Result<f32>
fn get_step_decreasing_power(&self) -> Result<f32>
fn get_term_criteria(&self) -> Result<TermCriteria>
fn get_term_criteria(&self) -> Result<TermCriteria>
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon).
See also
setTermCriteria