# Only one support vector in a linear svm kernel

I am new to SVM, but I would like to understand certain things.

Firstly, when dealing with multiclass classifications, I have a large number of support vectors as proven by R.

However, when I run svm.model in R

svm.model
We get the following:
Call:
svm(formula = churn ~ .,kernel = 'linear' , data = trainset, cost = 100, gamma = 1)
Parameters:
SVM-Type:  C-classification
SVM-Kernel:  linear
cost:  100
gamma: 1
Number of Support Vectors: 598


However, a coworker who is extremely technical mentioned that there is always ONLY 1 support vector in a linear kernel. Am I missing something or these 598 support vectors can actually be combined into a single vector?