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Machine Learning Towards Data with C...

Su, Runze.

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Machine Learning Towards Data with Complex Structures.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Towards Data with Complex Structures./
Author:	Su, Runze.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	93 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
Contained By:	Dissertations Abstracts International84-03A.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29322058
ISBN:	9798841767886

Machine Learning Towards Data with Complex Structures.
Su, Runze.

Machine Learning Towards Data with Complex Structures. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 93 p.

Source: Dissertations Abstracts International, Volume: 84-03, Section: A.

Thesis (Ph.D.)--Michigan State University, 2022.

The development of sequential analysis provides a deeper understanding in the exploration of many different fields. In the application of sequential analysis, there are two main challenges: How to extract informative features from a high-dimensional noisy domain? How to model the interaction for the information flow from multiple domains? We explored the two core challenges in bio-informatics, sales forecasting and multimedia services. In biology field, a typical problem is the to evaluate the interaction mechanism between non-coding DNA sequences and transcription. We propose CANEE, a convolutional self-attention architecture to analyze the function of non-coding DNA sequences. Compared to other existing models, CANEE achieves a better performance in overall prediction of 919 regulatory functions with respect to receiver operating characteristics and has a significant improvement on some responses in precision recall curve with shorter training time. In sales forecasting field, we extract a unique customers' microbehavior dependency structure from clickstream data based on a Word-to-Vector model. Then, we build a clickstream informed LSTM model to forecast the car sales over 30 days. Our model significantly outperforms the classic seasonal autoregressive integrated moving average model. Besides, we demonstrate that transfer knowledge among different car models can further improve the performance. Other applications for multi-domain sequences happens in multimedia service field, where we focus on the understanding of multiple domain modalities, we propose new principles for audio visual learning and introduce a new framework as well as its training algorithm to set sight of videos' themes to facilitate AVC learning.

ISBN: 9798841767886Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Machine learning

Machine Learning Towards Data with Complex Structures.
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245 1 0 $a Machine Learning Towards Data with Complex Structures.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 93 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
500 $a Advisor: Xie, Yuying.
502 $a Thesis (Ph.D.)--Michigan State University, 2022.
520 $a The development of sequential analysis provides a deeper understanding in the exploration of many different fields. In the application of sequential analysis, there are two main challenges: How to extract informative features from a high-dimensional noisy domain? How to model the interaction for the information flow from multiple domains? We explored the two core challenges in bio-informatics, sales forecasting and multimedia services. In biology field, a typical problem is the to evaluate the interaction mechanism between non-coding DNA sequences and transcription. We propose CANEE, a convolutional self-attention architecture to analyze the function of non-coding DNA sequences. Compared to other existing models, CANEE achieves a better performance in overall prediction of 919 regulatory functions with respect to receiver operating characteristics and has a significant improvement on some responses in precision recall curve with shorter training time. In sales forecasting field, we extract a unique customers' microbehavior dependency structure from clickstream data based on a Word-to-Vector model. Then, we build a clickstream informed LSTM model to forecast the car sales over 30 days. Our model significantly outperforms the classic seasonal autoregressive integrated moving average model. Besides, we demonstrate that transfer knowledge among different car models can further improve the performance. Other applications for multi-domain sequences happens in multimedia service field, where we focus on the understanding of multiple domain modalities, we propose new principles for audio visual learning and introduce a new framework as well as its training algorithm to set sight of videos' themes to facilitate AVC learning.
590 $a School code: 0128.
650 4 $a Statistics. $3 517247
650 4 $a Statistical physics. $3 536281
650 4 $a Information science. $3 554358
653 $a Machine learning
653 $a Data
653 $a Complex structure
690 $a 0463
690 $a 0800
690 $a 0723
690 $a 0217
710 2 $a Michigan State University. $b Statistics - Doctor of Philosophy. $3 3277963
773 0 $t Dissertations Abstracts International $g 84-03A.
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791 $a Ph.D.
792 $a 2022
793 $a English
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