Multi label text classification for un-trained data through supervised learning

World of digital data is growing at an aggressive rate, where every single minute new data is created and processed. All information retrieval processes are gone from insufficient to overflowing. It is doubling each and every year which makes information retrieval more challenging. Our focus is to t...

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Main Author:	BUDHIRAJA, Mayank
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2017
Subjects:	Multi label classification supervised learning un-trained data lexion MITB student Databases and Information Systems Data Science Numerical Analysis and Scientific Computing
Online Access:	https://ink.library.smu.edu.sg/sis_research/5991 https://doi.org/10.1109/I2C2.2017.8321804
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-6994
record_format	dspace
spelling	sg-smu-ink.sis_research-69942021-06-08T07:43:56Z Multi label text classification for un-trained data through supervised learning BUDHIRAJA, Mayank World of digital data is growing at an aggressive rate, where every single minute new data is created and processed. All information retrieval processes are gone from insufficient to overflowing. It is doubling each and every year which makes information retrieval more challenging. Our focus is to turn this immense data streams from a liability to our strengths. 2017-06-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/5991 info:doi/10.1109/I2C2.2017.8321804 https://doi.org/10.1109/I2C2.2017.8321804 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Multi label classification supervised learning un-trained data lexion MITB student Databases and Information Systems Data Science Numerical Analysis and Scientific Computing
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Multi label classification supervised learning un-trained data lexion MITB student Databases and Information Systems Data Science Numerical Analysis and Scientific Computing
spellingShingle	Multi label classification supervised learning un-trained data lexion MITB student Databases and Information Systems Data Science Numerical Analysis and Scientific Computing BUDHIRAJA, Mayank Multi label text classification for un-trained data through supervised learning
description	World of digital data is growing at an aggressive rate, where every single minute new data is created and processed. All information retrieval processes are gone from insufficient to overflowing. It is doubling each and every year which makes information retrieval more challenging. Our focus is to turn this immense data streams from a liability to our strengths.
format	text
author	BUDHIRAJA, Mayank
author_facet	BUDHIRAJA, Mayank
author_sort	BUDHIRAJA, Mayank
title	Multi label text classification for un-trained data through supervised learning
title_short	Multi label text classification for un-trained data through supervised learning
title_full	Multi label text classification for un-trained data through supervised learning
title_fullStr	Multi label text classification for un-trained data through supervised learning
title_full_unstemmed	Multi label text classification for un-trained data through supervised learning
title_sort	multi label text classification for un-trained data through supervised learning
publisher	Institutional Knowledge at Singapore Management University
publishDate	2017
url	https://ink.library.smu.edu.sg/sis_research/5991 https://doi.org/10.1109/I2C2.2017.8321804
_version_	1770575729806802944

Multi label text classification for un-trained data through supervised learning

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