國立虎尾科技大學 |

Multiple Learning for Generalized Linear Models in Big Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multiple Learning for Generalized Linear Models in Big Data./
作者:	Liu, Xiang.
面頁冊數:	1 online resource (84 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
Contained By:	Dissertations Abstracts International85-01A.
標題:	Web studies. -
電子資源:	click for full text (PQDT)
ISBN:	9798379837310

Multiple Learning for Generalized Linear Models in Big Data.
Liu, Xiang.

Multiple Learning for Generalized Linear Models in Big Data. - 1 online resource (84 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

Thesis (Ph.D.)--Purdue University, 2021.

Includes bibliographical references

Big data is an enabling technology in digital transformation. It perfectly complements ordinary linear models and generalized linear models, as training well-performed ordinary linear models and generalized linear models require huge amounts of data. With the help of big data, ordinary and generalized linear models can be well-trained and thus offer better services to human beings. However, there are still many challenges to address for training ordinary linear models and generalized linear models in big data. One of the most prominent challenges is the computational challenges. Computational challenges refer to the memory inflation and training inefficiency issues occurred when processing data and training models. Hundreds of algorithms were proposed by the experts to alleviate/overcome the memory inflation issues. However, the solutions obtained are locally optimal solutions. Additionally, most of the proposed algorithms require loading the dataset to RAM many times when updating the model parameters. If multiple model hyper-parameters needed to be computed and compared, e.g. ridge regression, parallel computing techniques are applied in practice. Thus, multiple learning with sufficient statistics arrays are proposed to tackle the memory inflation and training inefficiency issues.

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

Mode of access: World Wide Web

ISBN: 9798379837310Subjects--Topical Terms:

1148502
Web studies.
Index Terms--Genre/Form:

554714
Electronic books.

Multiple Learning for Generalized Linear Models in Big Data.
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504 $a Includes bibliographical references
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