Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Generalization Bounds for Some Ordinal Regression Shivani Agarwal
 

Summary: Generalization Bounds for Some Ordinal Regression
Algorithms
Shivani Agarwal
Massachusetts Institute of Technology, Cambridge MA 02139, USA
shivani@mit.edu
Abstract. The problem of ordinal regression, in which the goal is to learn a rule
to predict labels from a discrete but ordered set, has gained considerable atten-
tion in machine learning in recent years. We study generalization properties of
algorithms for this problem. We start with the most basic algorithms that work by
learning a real-valued function in a regression framework and then rounding off
a predicted real value to the closest discrete label; our most basic bounds for such
algorithms are derived by relating the ordinal regression error of the resulting
prediction rule to the regression error of the learned real-valued function. We end
with a margin-based bound for the state-of-the-art ordinal regression algorithm
of Chu & Keerthi (2007).
1 Introduction
In addition to the classical problems of classification and regression, several new types
of learning problems have emerged in recent years. Among these is the problem of or-
dinal regression, in which the goal is to learn a rule to predict labels of an ordinal scale,
i.e., labels from a discrete but ordered set. Ordinal regression is common in the social

  

Source: Agarwal, Shivani - Department of Computer Science and Automation, Indian Institute of Science, Bangalore

 

Collections: Computer Technologies and Information Sciences