Associated Namespace: IMAGINE
The operator defines and trains a Support Vector Machine (SVM) classifier which is used as an input for classifying data using the Classify Using Machine Intellect operator. All non-geometry attribute fields in the TrainingData feature data (except for ClassAttributeName) will be used for training. The Select Attributes or Remove Attributes operator can be used to tailor the training data's attribute schema before the training data is used in the Initialize SVM operator.
Support Vector Machine (SVM) is a supervised machine learning algorithm that performs classification by finding optimal hyper planes that separates the classes. The minimum distance between a hyper plane and a class is called a Margin. The optimal hyper plane is the one which has the maximum margin.
The training data points that lie near the dividing hyper plane, and if removed would change the position of the hyper plane, are called support vectors.
The data to be used for defining and training the classifier
The attribute field from TrainingData which defines the classes into which the resulting MachineIntellect may classify features.
Seed for the random number generator that is used during the training. Fixing the seed at a specific value allows repeated executions of the operator with the same input data to produce the exact same results.
Empty. The trained SVM classifier is random. That is, running this operator multiple times will generate (slightly) different results.
The trained SVM classifier