國立虎尾科技大學 |

Machine Learning Towards Data with Complex Structures.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning Towards Data with Complex Structures./
作者:	Su, Runze.
面頁冊數:	1 online resource (93 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
Contained By:	Dissertations Abstracts International84-03A.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798841767886

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

Machine Learning Towards Data with Complex Structures. - 1 online resource (93 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798841767886Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Towards Data with Complex Structures.
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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.
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