東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Advances in Machine Learning for Complex Structured Functional Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in Machine Learning for Complex Structured Functional Data./
作者:	Tang, Chengliang.
面頁冊數:	1 online resource (125 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29328417click for full text (PQDT)
ISBN:	9798841772712

Advances in Machine Learning for Complex Structured Functional Data.
Tang, Chengliang.

Advances in Machine Learning for Complex Structured Functional Data. - 1 online resource (125 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--Columbia University, 2022.

Includes bibliographical references

Functional data analysis (FDA) refers to a broad collection of statistical and machine learning methods that deal with the data in the form of random functions. In general, functional data are assumed to lie in a constrained functional space, e.g., images, and smooth curves, rather than the conventional Euclidean space, e.g., scalar vectors. The explosion of massive data and high-performance computational resources brings exciting opportunities as well as new challenges to this field. On one hand, the rich information from modern functional data enables an investigation into the underlying data patterns at an unprecedented scale and resolution. On the other hand, the inherent complex structures and huge data sizes of modern functional data pose additional practical challenges to model building, model training, and model interpretation under various circumstances.This dissertation discusses recent advances in machine learning for analyzing complex structured functional data. Chapter 1 begins with a general introduction to examples of modern functional data and related data analysis challenges. Chapter 2 introduces a novel machine learning framework, artificial perceptual learning (APL), to tackle the problem of weakly supervised learning in functional remote sensing data. Chapter 3 develops a flexible function-on-scalar regression framework, Wasserstein distributional learning (WDL), to address the challenge of modeling density functional outputs. Chapter 4 concludes the dissertation and discusses future directions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841772712Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

542853
Electronic books.

Advances in Machine Learning for Complex Structured Functional Data.
LDR:02835nmm a2200373K 4500 001 2356475
005 20230612110832.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798841772712
035 $a (MiAaPQ)AAI29328417
035 $a AAI29328417
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Tang, Chengliang. $3 3696947
245 1 0 $a Advances in Machine Learning for Complex Structured Functional Data.
264 0 $c 2022
300 $a 1 online resource (125 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Zheng, Tian.
502 $a Thesis (Ph.D.)--Columbia University, 2022.
504 $a Includes bibliographical references
520 $a Functional data analysis (FDA) refers to a broad collection of statistical and machine learning methods that deal with the data in the form of random functions. In general, functional data are assumed to lie in a constrained functional space, e.g., images, and smooth curves, rather than the conventional Euclidean space, e.g., scalar vectors. The explosion of massive data and high-performance computational resources brings exciting opportunities as well as new challenges to this field. On one hand, the rich information from modern functional data enables an investigation into the underlying data patterns at an unprecedented scale and resolution. On the other hand, the inherent complex structures and huge data sizes of modern functional data pose additional practical challenges to model building, model training, and model interpretation under various circumstances.This dissertation discusses recent advances in machine learning for analyzing complex structured functional data. Chapter 1 begins with a general introduction to examples of modern functional data and related data analysis challenges. Chapter 2 introduces a novel machine learning framework, artificial perceptual learning (APL), to tackle the problem of weakly supervised learning in functional remote sensing data. Chapter 3 develops a flexible function-on-scalar regression framework, Wasserstein distributional learning (WDL), to address the challenge of modeling density functional outputs. Chapter 4 concludes the dissertation and discusses future directions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Statistical physics. $3 536281
653 $a Machine learning
653 $a Complex structured
653 $a Functional data
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0463
690 $a 0800
690 $a 0217
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Columbia University. $b Statistics. $3 1682152
773 0 $t Dissertations Abstracts International $g 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29328417 $z click for full text (PQDT)