방송통신대학교와 통계학회 분류연구회에서는 Erasmus University Rotterdam의 Econometric Institute에 director로 계신 Patrick Groenen 교수를 모시고 세미나를 다음과 같이 개최할 예정입니다.

일시 : 2016년 11월 2일 오후 4시

장소 : 방송대 5층 세미나실 534호

Title: A Regression Perspective of Binary and Multi-Class Support Vector Machines

Authors: Patrick Groenen and Gertjan van den Burg

Affilition: Econometric Institute, Erasmus University Rotterdam

Support vector machines (SVMs) have become a standard tool for binary classification problems that has become increasingly popular. In the machine learning literature, the SVM is often explained through the dual of a convex optimization problem. Instead, we approach it as a regression problem with a specific error function and a ridge type penalty term.

However, in the case that more than two classes need to be predicted often a series of binary SVMs are performed (one-versus-all or between all pairs of classes, one-versus-one). A disadvantage of such methods is that they are heuristics that do not simultaneously estimate all parameters in a single model. We discuss a new multiclass SVM loss function (GenSVM). As with the binary SVM, an object that is predicted to be nearest to its class receives a zero error and if the object is closer to another class the error consists of a function of the distance to the zero-error region. This general loss function has the binary SVM and some existing multiclass SVM loss functions as special cases.

We will discuss these methods and provide an illustrative example where the aim is to predict college degree from genotype data.

Keywords: Classification, supervised learning, support vector machine (SVM), majorization, MM, CCP