Machine Learning, Logistic Regression, Non Linear.
that belong to class A from those that do not belong to class A. c are the parameters to be estimated. Consider a decision boundary that can be expressed as a polynomial in feature variables but linear in the weights/coefficients. This case lends itself to be modeled within the (linear) framework