Question

After training an SVM, we can discard all examples which do not support vectors and can still classify new examples?
A) TRUE
B) FALSE

Answer 1

Solution: A

This is true because only support vectors affect the boundary.

Answer 2

SVM or Support Vector machines, are suited for working with small data sets, and they produce faster and more detailed models based in the dataset.

In a SVM system, the support vectors are the vectors which are responsible for producing the output result.

Therefore if we discard all examples which do not support vectors, we can still classify as the output is based on support vectors.

Therefore the answer is True.