In words:
When you feed to a SVM classifier, let's say using a tool like Weka, you can of course feed more than one feature, thus a vector of more than one column, which will become the features on which you classify (data will also hopefully have more than one row).
CS Notes:
Input: set of (input, output) training pair samples; call the input sample features x1, x2…xn, and the output result y. Typically, there can be lots of input features xi.
Output: set of weights w (or wi), one for each feature, whose linear combination predicts the value of y.
"Math" notes:
The feature space is typically infinite-dimensional.
The optimal hyperplane stems from the function class with the lowest number of independent features.
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http://www.cs.waikato.ac.nz/ml/weka/downloading.html
https://www.cise.ufl.edu/class/cis4930sp11dtm/notes/intro_svm_new.pdf
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