Recall that a maximum margin classifier (e.g., a support vector machine) separates data points from two classes while maximizing the margin of separation.
a maximum margin hyperplane is defined by w • x − b = 0, where x is a feature vector representing an arbitrary data point, and w (a weight vector) and b (a scalar) are parameters that are learned by solving the following constrained optimization problem: where c i ∈ {+1, −1} is the class of the i-th training point x i , ξ i is the degree of misclassification of x i , and C is a regularization parameter that balances training error and model complexity.