Usually the SVM model uses support vectors to define the model function. However, for the case of a linear function
(linear kernel type) the function is a linear hyperplane that can be more efficiently expressed using the
coefficients of all mining fields. In this case, no support vectors are required at all, and hence SupportVectors
will be absent and only the Coefficients element is necessary.
The SVM representation specifies which of both representations is used:
Linear Supertypes
Enumeration, Serializable, Serializable, AnyRef, Any
Usually the SVM model uses support vectors to define the model function. However, for the case of a linear function (linear kernel type) the function is a linear hyperplane that can be more efficiently expressed using the coefficients of all mining fields. In this case, no support vectors are required at all, and hence SupportVectors will be absent and only the Coefficients element is necessary.
The SVM representation specifies which of both representations is used: