Error-correcting codes and applications to large scale classification systems
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/53140 |
| _version_ | 1811089960910979072 |
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| author | Hurwitz, Jeremy Scott |
| author2 | Ahmad Abdulkader and Tomas Lozano-Perez. |
| author_facet | Ahmad Abdulkader and Tomas Lozano-Perez. Hurwitz, Jeremy Scott |
| author_sort | Hurwitz, Jeremy Scott |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. |
| first_indexed | 2024-09-23T14:28:23Z |
| format | Thesis |
| id | mit-1721.1/53140 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:28:23Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/531402019-04-12T11:15:00Z Error-correcting codes and applications to large scale classification systems Hurwitz, Jeremy Scott Ahmad Abdulkader and Tomas Lozano-Perez. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Includes bibliographical references (p. 37-39). 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. by Jeremy Scott Hurwitz. M.Eng. 2010-03-25T15:06:13Z 2010-03-25T15:06:13Z 2009 2009 Thesis http://hdl.handle.net/1721.1/53140 505516307 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 39 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Hurwitz, Jeremy Scott Error-correcting codes and applications to large scale classification systems |
| title | Error-correcting codes and applications to large scale classification systems |
| title_full | Error-correcting codes and applications to large scale classification systems |
| title_fullStr | Error-correcting codes and applications to large scale classification systems |
| title_full_unstemmed | Error-correcting codes and applications to large scale classification systems |
| title_short | Error-correcting codes and applications to large scale classification systems |
| title_sort | error correcting codes and applications to large scale classification systems |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/53140 |
| work_keys_str_mv | AT hurwitzjeremyscott errorcorrectingcodesandapplicationstolargescaleclassificationsystems |