IMAQ Train SVM VI

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Updated2023-02-21
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Owning Palette: Classifier Engines
Requires: NI Vision Development Module

Sets the Classifier Session to use the SVM Classifier engine, and configures the SVM parameters it will use.

IMAQ Train SVM

Classifier Session is the reference to the classifier session on which this VI operates.

Kernel Options is a cluster of parameters to configure the kernel to be used.

Kernel specifies the kernel that the classifier uses to determine if a sample is a texture or contains a defect. The default value is RBF.

The SVM classifier is a linear classifier. Use a nonlinear kernel to transform samples with nonlinear feature information to a dimension where the feature information is linearly separable. Refer to the NI Vision Concepts Help for more information about kernels and one-class SVM classifiers. The following options are available.

Linear (0)	Applies a linear kernel to the sample. Use this kernel If the number of features per sample is high.
Polynomial (1)	Applies a polynomial kernel to the sample.
Gaussian (2)	Applies a Gaussian kernel to the sample.
RBF (3)	(Default) Applies a radial basis function (RBF) kernel to the sample.

Degree specifies the degree of the polynomial kernel. The polynomial kernel becomes a linear kernel if you specify a degree of 1. Refer to the NI Vision Concepts Help for more information about kernel parameters and one-class SVM classifiers.

Gamma specifies the gamma value for the polynomial and RBF kernels. A high value requires more support vectors to classify the sample. Use a high value for samples with regularly distributed feature information, and a low value for samples with irregularly distributed feature information. Refer to the NI Vision Concepts Help for more information about kernel parameters and one-class SVM classifiers.

Coefficient specifies the coefficient of the polynomial kernel. Refer to the NI Vision Concepts Help for more information about kernel parameters and one-class SVM classifiers.

Sigma specifies the sigma value for the Gaussian kernel. The default value is 1. A higher value produces a smoother Gaussian function and fewer support vectors. Use a high value for samples with regularly distributed feature information, and a low value for samples with irregularly distributed feature information. Refer to the NI Vision Concepts Help for more information about kernel parameters and one-class SVM classifiers.

Cache(MB) specifies the cache size, in megabytes, for kernel operations.

Model Options is a cluster of parameter to configures the SVM model.

SVM Model specifies the SVM model that will be used to train the classifier. Refer to the NI Vision Concepts Help for information about support vector machine models.

C-SVC (0)	The C-SVC model allows the SVM algorithm to clearly separate samples that are separated by a very narrow margin. If the SVM algorithm cannot define a clear margin, it uses the cost parameter to allow some training errors and produce a soft margin. If the cost value is too high it prohibits training errors, producing a narrow margin and rigid classification.
nu-SVC (1)	In the Nu-SVC model, the Nu parameter controls training errors and the number of support vectors. The nu value specifies both the maximum ratio of training errors and the minimum number of support vectors relative to the number of samples. Nu must be greater than 0 and cannot exceed 1. A higher nu value increases tolerance for variation in the texture, but may also increase tolerance for texture defects. If nu is too high, training produces too many training errors to be useful.
One Class (2)	(Default) In the one-class model, the SVM algorithm considers the spatial distribution information for each sample to determine whether the sample belongs to the known class.

Tolerance specifies the maximum gradient of the quadratic function used to compute support vectors. The default value is 0.001. You typically do not need to change this value. Refer to the NI Vision Concepts Help for more information about tolerance and one-class SVM classifiers.

Max. Iteration specifies a maximum number of iterations.

Nu specifies the value of Nu. Values range from 0.1 to 1. The default value is 0.1 A higher Nu value increases tolerance for variation in the texture, but may also increase tolerance for defects. If the texture classifier does not perform as expected because the trained texture samples do not represent every possible variation of the texture, try increasing the value of Nu. Refer to the NI Vision Concepts Help for more information about nu and one-class SVM classifiers.

Shrinking When enabled, the classifier uses shrinking heuristics to attempt to reduce the number of variables involved in the classification computation. Shrinking may reduce processing time if the number of iterations is large.

Cost specifies the penalty for training errors. If the cost value is too high it prohibits training errors, producing a narrow margin and rigid classification. Decrease the cost value to allow more training errors and produce a softer margin between classes.

Weighted Cost is the cost weight for each label.

	Label is the label associated with the Weight coefficient.
	Weight is the weighted coefficient for the Label.

error in (no error) describes the error status before this VI or function runs. The default is no error. If an error occurred before this VI or function runs, the VI or function passes the error in value to error out. This VI or function runs normally only if no error occurred before this VI or function runs. If an error occurs while this VI or function runs, it runs normally and sets its own error status in error out. Use the Simple Error Handler or General Error Handler VIs to display the description of the error code. Use error in and error out to check errors and to specify execution order by wiring error out from one node to error in of the next node.

	status is TRUE (X) if an error occurred before this VI or function ran or FALSE (checkmark) to indicate a warning or that no error occurred before this VI or function ran. The default is FALSE.
	code is the error or warning code. If status is TRUE, code is a nonzero error code. If status is FALSE, code is 0 or a warning code.
	source describes the origin of the error or warning and is, in most cases, the name of the VI or function that produced the error or warning. The default is an empty string.

Classifier Session (dup) is the reference to the classifier session this VI creates.

Training Results is an array of statistical information for each class in the classifier.

	Class is the class into which the classifier session categorizes the input sample.
	Standard Deviation is the standard deviation from the mean of all samples in Class.
	Number of Samples is the number of samples in Class.
	Number of SVs is the number of support vectors in Class.

Class Distance Table is a table giving the mean distance from each class to each other class.

error out contains error information. If error in indicates that an error occurred before this VI or function ran, error out contains the same error information. Otherwise, it describes the error status that this VI or function produces. Right-click the error out indicator on the front panel and select Explain Error from the shortcut menu for more information about the error.

	status is TRUE (X) if an error occurred or FALSE (checkmark) to indicate a warning or that no error occurred.
	code is the error or warning code. If status is TRUE, code is a nonzero error code. If status is FALSE, code is 0 or a warning code.
	source describes the origin of the error or warning and is, in most cases, the name of the VI or function that produced the error or warning. The default is an empty string.

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IMAQ Train SVM VI

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