讲座题目：Conformal Predictors and Their Applications
主 讲 人：骆志远教授
Several techniques such as Support Vector Machine (SVM) have been developed to tackle the problem of dimensionality by transferring the problem into high-dimensional space, and solving it in that space. They based on so-called kernel methods and can very often solve some high-dimensional problems. However, one drawback of these successful techniques is their lack of ability to provide rigorous confidence measures for the predictions they make. Recently a new set of techniques, called Conformal Predictors, have been developed that allows to make valid predictions and supply useful measures of confidence. The approach is based on recently developed approximations to the universal measures of confidence given by the algorithmic theory of randomness. The talk will describe the techniques and illustrate with some applications.