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

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 a reference to the session referenced by Classifier Session.

Nearest Neighbor options out are the options used in the IMAQ Train Nearest Neighbor VI.

Note  Refer to the NI Vision Concepts Manual for more information about these options.

	Method is the nearest neighbor classification method used. The following options are valid: Nearest Neighbor (0) This is the most direct approach to classification. In Nearest Neighbor classification, the distance of an input sample of unknown class to another class is defined as the distance to the closest samples that are used to represent that class. K Nearest Neighbor (1) This is more robust to noise compared with nearest neighbor classification. In K-Nearest Neighbor classification, an input feature vector is classified into a class based on a voting mechanism. The NI Classifier finds K nearest samples from all the classes. The input feature vector of unknown class is assigned to the class with majority of the votes in the K nearest samples. Minimum Mean Distance (2) This is most effective in applications that have little or no feature pattern visibility or other corruptive influences. In Minimum Mean Distance classification, an input feature vector of unknown class is classified based on its distance to each class center.
	Metric is the distance metric used. The following options are valid: Maximum (0) This is the metric most sensitive to small variations between samples. Use Maximum when you need to classify samples with very small differences into different classes. Sum (1) (Also known as the Manhattan metric or Taxicab metric) This is the metric used in most classification applications. This is the default value. Euclidean (2) This is the metric least sensitive to small variations between samples. Use Euclidean when you need to classify samples with small differences into the same class.
	k is the k value used if Method is set to K Nearest Neighbor. If Method is not set to K Nearest Neighbor, this value is ignored.

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.