Image Miner : an architecture to support deep mining of images

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Zhang, Edwin Meng

Advisor dc:contributor.advisor

• Kalyan Veeramachaneni.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100612

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100612