자료유형  

 학위논문

Control Number  

0016932687

International Standard Book Number  

9798379837310

Dewey Decimal Classification Number  

301.424

Main Entry-Personal Name  

Liu, Xiang.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2021

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2021

Physical Description  

1 online resource(84 p.)

General Note  

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

General Note  

Advisor: Yang, Baijian;Zhang, Tonglin.

Dissertation Note  

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

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Big 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.

Subject Added Entry-Topical Term  

Independent variables.

Subject Added Entry-Topical Term  

Mean square errors.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Random access memory.

Subject Added Entry-Topical Term  

Dependent variables.

Subject Added Entry-Topical Term  

Iterative methods.

Subject Added Entry-Topical Term  

Optimization techniques.

Subject Added Entry-Topical Term  

Disk drives.

Subject Added Entry-Topical Term  

Normal distribution.

Subject Added Entry-Topical Term  

Data processing.

Subject Added Entry-Topical Term  

Hard disks.

Subject Added Entry-Topical Term  

University students.

Subject Added Entry-Topical Term  

Cloud computing.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Higher education.

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Information technology.

Subject Added Entry-Topical Term  

Mathematics.

Subject Added Entry-Topical Term  

Web studies.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 85-01A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:643320