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?