國立虎尾科技大學 |

Selected Topics in Deep Learning and Text Mining.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Selected Topics in Deep Learning and Text Mining./
Author:	Wang, Baiyang.
Description:	1 online resource (114 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Subject:	Industrial engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355824551

Selected Topics in Deep Learning and Text Mining.
Wang, Baiyang.

Selected Topics in Deep Learning and Text Mining. - 1 online resource (114 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--Northwestern University, 2018.

Includes bibliographical references

The thesis contains all four chapters of my Ph.D. research on deep learning and text mining. The first chapter, "Temporal Topic Analysis with Endogenous and Exogenous Processes'', proposes a topic model which mines temporal economy-related documents with an exogenous economic indicator, and finds the relationship between document topics and the economic background. The second chapter, "Regularization for Unsupervised Deep Neural Nets'', discusses and compares different regularization methods for unsupervised deep neural nets, such as deep belief networks, and proposes a new approach to refine Dropout. The third chapter, "An Attention-Based Deep Net for Learning to Rank'', proposes a list-wise attention-based learning-to-rank mechanism for image and document retrieval. The fourth chapter, "Generative Adversarial Nets for Multiple Text Corpora'', proposes two applications of the generative adversarial net on text data with multiple corpora, i.e. refining word embeddings and generating multi-corpora document embeddings.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355824551Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Selected Topics in Deep Learning and Text Mining.
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100 1 $a Wang, Baiyang. $3 1184444
245 1 0 $a Selected Topics in Deep Learning and Text Mining.
264 0 $c 2018
300 $a 1 online resource (114 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Diego Klabjan.
502 $a Thesis (Ph.D.)--Northwestern University, 2018.
504 $a Includes bibliographical references
520 $a The thesis contains all four chapters of my Ph.D. research on deep learning and text mining. The first chapter, "Temporal Topic Analysis with Endogenous and Exogenous Processes'', proposes a topic model which mines temporal economy-related documents with an exogenous economic indicator, and finds the relationship between document topics and the economic background. The second chapter, "Regularization for Unsupervised Deep Neural Nets'', discusses and compares different regularization methods for unsupervised deep neural nets, such as deep belief networks, and proposes a new approach to refine Dropout. The third chapter, "An Attention-Based Deep Net for Learning to Rank'', proposes a list-wise attention-based learning-to-rank mechanism for image and document retrieval. The fourth chapter, "Generative Adversarial Nets for Multiple Text Corpora'', proposes two applications of the generative adversarial net on text data with multiple corpora, i.e. refining word embeddings and generating multi-corpora document embeddings.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Artificial intelligence. $3 559380
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0800
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Northwestern University. $b Industrial Engineering and Management Sciences. $3 1180832
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