Error-correcting codes and applications to large scale classification systems
Name
505516307-MIT.pdf
Description
Full printable version
Size
3 MB
Format
Adobe PDF
Checksum (MD5)
c207c4d054fddeed82165191c417c1e8
Author(s)
Hurwitz, Jeremy Scott
Advisor(s)
Ahmad Abdulkader and Tomas Lozano-Perez.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we study the performance of distributed output coding (DOC) and error-Correcting output coding (ECOC) as potential methods for expanding the class of tractable machine-learning problems. Using distributed output coding, we were able to scale a neural-network-based algorithm to handle nearly 10,000 output classes. In particular, we built a prototype OCR engine for Devanagari and Korean texts based upon distributed output coding. We found that the resulting classifiers performed better than existing algorithms, while maintaining small size. Error-correction, however, was found to be ineffective at increasing the accuracy of the ensemble. For each language, we also tested the feasibility of automatically finding a good codebook. Unfortunately, the results in this direction were primarily negative.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (p. 37-39).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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