Summary: Incorporating Test Inputs into Learning
Zehra Cataltepe
Learning Systems Group
Department of Computer Science
California Institute of Technology
Pasadena, CA 91125
zehra@cs.caltech.edu
Malik Magdon­Ismail
Learning Systems Group
Department of Electrical Engineering
California Institute of Technology
Pasadena, CA 91125
magdon@cco.caltech.edu
Abstract
In many applications, such as credit default prediction and medical im­
age recognition, test inputs are available in addition to the labeled train­
ing examples. We propose a method to incorporate the test inputs into
learning. Our method results in solutions having smaller test errors than
that of simple training solution, especially for noisy problems or small
training sets.

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology
Magdon-Ismail, Malik - Department of Computer Science, Rensselaer Polytechnic Institute

Collections: Computer Technologies and Information Sciences