2026-09-18T19:08:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1006122026-06-06T01:03:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kalyan Veeramachaneni.
Zhang, Edwin Meng
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-01-04T19:58:37Z
2016-01-04T19:58:37Z
2015
2015
http://hdl.handle.net/1721.1/100612
932619957
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
70 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Image Miner : an architecture to support deep mining of images
ImageMiner : an architecture to support deep mining of images
Thesis
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<Abstract>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.</Abstract>
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