1. A method for training a support vector machine plus (SVM+) using sequential maximization, comprising: selecting a working set of two indexes for a target function to create a quadratic function depending on a number of variables; reducing the number of variables to two variables in the quadratic function using linear constraints; computing an extreme point for the quadratic function in closed form; defining a two-dimensional set where the indexes determine whether a data point is in the two-dimensional set or not; determining whether the extreme point belongs to the two-dimensional set wherein: if the extreme point belongs to the two-dimensional set, the extreme point defines a maximum and the extreme point defines a new set of parameters for a next iteration, and otherwise, restricting the quadratic function on at least one boundary of the two-dimensional set to create a one-dimensional quadratic function; and repeating the steps until the maximum is determined.