Using distributed machine learning to predict arterial blood pressure
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2014
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| Online Access: | http://hdl.handle.net/1721.1/91441 |
| _version_ | 1826202528256098304 |
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| author | Emeagwali, Ijeoma |
| author2 | Una-May O'Reilly and Erik Hemberg. |
| author_facet | Una-May O'Reilly and Erik Hemberg. Emeagwali, Ijeoma |
| author_sort | Emeagwali, Ijeoma |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. |
| first_indexed | 2024-09-23T12:08:55Z |
| format | Thesis |
| id | mit-1721.1/91441 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:08:55Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/914412019-04-11T01:26:04Z Using distributed machine learning to predict arterial blood pressure Emeagwali, Ijeoma Una-May O'Reilly and Erik Hemberg. 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., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. 3 Cataloged from PDF version of thesis. Includes bibliographical references (page 57). This thesis describes how to build a flow for machine learning on large volumes of data. The end result is EC-Flow, an end to end tool for using the EC-Star distributed machine learning system. The current problem is that analysing datasets on the order of hundreds of gigabytes requires overcoming many engineering challenges apart from the theory and algorithms used in performing the machine learning and analysing the results. EC-Star is a software package that can be used to perform such learning and analysis in a highly distributed fashion. However, there are many complexities to running very large datasets through such a system that increase its difficulty of use because the user is still exposed to the low level engineering challenges inherent to manipulating big data and configuring distributed systems. EC-Flow attempts to abstract a way these difficulties, providing users with a simple interface for each step in the machine learning pipepline. by Ijeoma Emeagwali. M. Eng. 2014-11-04T21:36:55Z 2014-11-04T21:36:55Z 2014 2014 Thesis http://hdl.handle.net/1721.1/91441 893676027 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 57 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Emeagwali, Ijeoma Using distributed machine learning to predict arterial blood pressure |
| title | Using distributed machine learning to predict arterial blood pressure |
| title_full | Using distributed machine learning to predict arterial blood pressure |
| title_fullStr | Using distributed machine learning to predict arterial blood pressure |
| title_full_unstemmed | Using distributed machine learning to predict arterial blood pressure |
| title_short | Using distributed machine learning to predict arterial blood pressure |
| title_sort | using distributed machine learning to predict arterial blood pressure |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/91441 |
| work_keys_str_mv | AT emeagwaliijeoma usingdistributedmachinelearningtopredictarterialbloodpressure |