Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)
Zehra Cataltepe, Malik Magdon-Ismail
In many applications, such as credit default prediction and medical im(cid:173) age recognition, test inputs are available in addition to the labeled train(cid:173) 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.