There is increasing interest in discovering individualized treatment rules for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal individualized treatment rule which is a deterministic function of patient specific characteristics maximizing expected clinical outcome. Zhao et al. (2012) shown that estimating such an optimal treatment regime is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome. Then they propose an outcome weighted learning (OWL) approach based on the support vector machine framework. A few other development after the original OWL will also be in this talk.