Image Miner : an architecture to support deep mining of images
Name
932619957-MIT.pdf
Description
Full printable version
Size
2.78 MB
Format
Adobe PDF
Checksum (MD5)
6e0cb50192ebb9f7d3186964795e1674
Author(s)
Zhang, Edwin Meng
Advisor(s)
Kalyan Veeramachaneni.
Alternative Title
ImageMiner : an architecture to support deep mining of images
Date Issued
2015
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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