LAGO, SVM and Rare Target Detection
Speaker: Mu Zhu (Statistics and Actuarial Sciences, UW)
I shall describe a few projects in the area of rare target detection.
First, I will go over the main ideas of LAGO, a new algorithm we
developed that is much more efficient than SVM and KNN for rare target
problems. Second, I will discuss a new experiment in which we try to
select training observations sequentially in order to build a good
SVM. Our main area of application so far has been drug discovery, but
the methods developed and lessons being learned are generally
applicable to other rare target problems. Other than myself, people
involved in these projects include: Wanhua Su, Michelle Zhou, Hugh
Chipman and Stan Young.