Image Miner : an architecture to support deep mining of images
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Colle...
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ndltd-MIT-oai-dspace.mit.edu-1721.1-1006122019-05-02T15:48:47Z Image Miner : an architecture to support deep mining of images ImageMiner : an architecture to support deep mining of images Zhang, Edwin Meng Kalyan Veeramachaneni. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 69-70). In this thesis, I designed a cloud based system, called ImageMiner, to tune parameters of feature extraction process in a machine learning pipeline for images. Feature extraction is a key component of the machine learning pipeline, and tune its parameters to extract the best features can have significant effect on the accuracy achieved by the machine learning system. To enable scalable parameter tuning, I designed a master-slave architecture to run on the Amazon cloud. To overcome the computational bottlenecks due to large datasets, I used a data parallel approach where each worker runs independently on a subset of data. The worker uses a Gaussian Copula Process to tune parameters and determines the best set of parameters and model to use. by Edwin Meng Zhang. M. Eng. in Computer Science and Engineering 2016-01-04T19:58:37Z 2016-01-04T19:58:37Z 2015 2015 Thesis http://hdl.handle.net/1721.1/100612 932619957 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 70 pages application/pdf Massachusetts Institute of Technology |
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Electrical Engineering and Computer Science. Zhang, Edwin Meng Image Miner : an architecture to support deep mining of images |
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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Cataloged from student-submitted PDF version of thesis. === Includes bibliographical references (pages 69-70). === In this thesis, I designed a cloud based system, called ImageMiner, to tune parameters of feature extraction process in a machine learning pipeline for images. Feature extraction is a key component of the machine learning pipeline, and tune its parameters to extract the best features can have significant effect on the accuracy achieved by the machine learning system. To enable scalable parameter tuning, I designed a master-slave architecture to run on the Amazon cloud. To overcome the computational bottlenecks due to large datasets, I used a data parallel approach where each worker runs independently on a subset of data. The worker uses a Gaussian Copula Process to tune parameters and determines the best set of parameters and model to use. === by Edwin Meng Zhang. === M. Eng. in Computer Science and Engineering |
author2 |
Kalyan Veeramachaneni. |
author_facet |
Kalyan Veeramachaneni. Zhang, Edwin Meng |
author |
Zhang, Edwin Meng |
author_sort |
Zhang, Edwin Meng |
title |
Image Miner : an architecture to support deep mining of images |
title_short |
Image Miner : an architecture to support deep mining of images |
title_full |
Image Miner : an architecture to support deep mining of images |
title_fullStr |
Image Miner : an architecture to support deep mining of images |
title_full_unstemmed |
Image Miner : an architecture to support deep mining of images |
title_sort |
image miner : an architecture to support deep mining of images |
publisher |
Massachusetts Institute of Technology |
publishDate |
2016 |
url |
http://hdl.handle.net/1721.1/100612 |
work_keys_str_mv |
AT zhangedwinmeng imagemineranarchitecturetosupportdeepminingofimages |
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1719028659755417600 |