A system analysis of improvements in machine learning
Name
932082841-MIT.pdf
Description
Full printable version
Size
5.02 MB
Format
Adobe PDF
Checksum (MD5)
2060e0e1352e7b76ed13a1b4a6468d16
Author(s)
Thomas, Sabin M. (Sabin Mammen)
Advisor(s)
Abel Sanchez.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
Machine learning algorithms used for natural language processing (NLP) currently take too long to complete their learning function. This slow learning performance tends to make the model ineffective for an increasing requirement for real time applications such as voice transcription, language translation, text summarization topic extraction and sentiment analysis. Moreover, current implementations are run in an offline batch-mode operation and are unfit for real time needs. Newer machine learning algorithms are being designed that make better use of sampling and distributed methods to speed up the learning performance. In my thesis, I identify unmet market opportunities where machine learning is not employed in an optimum fashion. I will provide system level suggestions and analyses that could improve the performance, accuracy and relevance.
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, February 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 50-51).
Subjects
Engineering Systems Division.
System Design and Management Program.
MIT Department
System Design and Management Program.
Massachusetts Institute of Technology. Engineering Systems Division
Terms of Use
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