. Author manuscript; available in PMC: 2012 Sep 30.

Published in final edited form as: J Neurosci Methods. 2011 Jul 27;201(1):196–203. doi: 10.1016/j.jneumeth.2011.06.027

Algorithm 3.

(Training and prediction with the support vector machine classifier)

Training (model generation) stage
1.	Initialize w = 0
2.	Find w as solution of optimization problem min ∥w∥ subject to $(\begin{matrix} y_{i} (x_{i}^{T} w) \geq 1 - ξ \\ ξ_{i} \geq 0, & \sum ξ_{i} \leq C (constant) \end{matrix})$ .
where ξ’s are slack variables of optimization problem (in non-separable case).
Prediction (testing) stage
1.	Compute w^Tx
2.	Predict y = sign(w^Tx)