A system analysis of improvements in machine learning

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Thomas, Sabin M. (Sabin Mammen)

Advisor dc:contributor.advisor

• Abel Sanchez.

• Engineering Systems Division.
• System Design and Management Program.

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/1721.1/100386

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100386