2026-09-18T23:17:55Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1003862022-01-13T07:54:46Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Abel Sanchez. Thomas, Sabin M. (Sabin Mammen) System Design and Management Program. System Design and Management Program. Massachusetts Institute of Technology. Engineering Systems Division 2015-12-16T16:35:47Z 2015-12-16T16:35:47Z 2014 2015 http://hdl.handle.net/1721.1/100386 932082841 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). 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. by Sabin M. Thomas. S.M. in Engineering and Management 53 pages eng Massachusetts Institute of Technology 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 Engineering Systems Division. System Design and Management Program. A system analysis of improvements in machine learning Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="76cae49c-843a-44f1-ba13-176a15549bed"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A system analysis of improvements in machine learning</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Thomas, Sabin M. (Sabin Mammen)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering Systems Division.</Keyword> <Keyword>System Design and Management Program.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>