Accession Number:

ADA454942

Title:

Regularization Through Feature Knock Out

Descriptive Note:

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE MA CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING

Personal Author(s):

Report Date:

2004-11-01

Pagination or Media Count:

12.0

Abstract:

In this paper, we present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of the original data. The motivation is that since the learning algorithm lacks information about which parts of the data are reliable, it has to produce more robust classification functions. We then demonstrate how this regularization leads to redundancy in the resulting classifiers, which is somewhat in contrast to the common interpretations of the Occams razor principle. Using this framework, we propose a simple addition to the gentle boosting algorithm which enables it to work with only a few examples. We test this new algorithm on a variety of datasets and show convincing results.

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE