1. A system for training a text classifier, the system comprising: a memory storing computer-executable instructions that implement: a text data preprocessor that preprocesses raw training text to produce an input matrix, the raw training text including documents and indications of whether each document is a positive or a negative training example of a classification; and a module for solving a weighted proximal support vector machine equation comprising: a weighting module that generates a weighted matrix by re-weighting the input matrix based on how many training examples are positive and how many training examples are negative wherein the weighting is based on satisfying the following equation: 
 N + δ + 2 =N − δ − 2 where N + and N − denote numbers of positive and negative training examples and δ + and δ − denote weights of the positive and negative training examples; and a model-vector generator that iteratively calculates a model vector based on the weighted matrix using a proximal support vector machine model; and a processor for executing the computer-executable instructions stored in the memory.