語系:
繁體中文
English
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
Domain Adaptation through Optimal Tr...
~
Gao, Keyue.
FindBook
Google Book
Amazon
博客來
Domain Adaptation through Optimal Transport with Class Imbalance.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Domain Adaptation through Optimal Transport with Class Imbalance./
作者:
Gao, Keyue.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
71 p.
附註:
Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Contained By:
Dissertations Abstracts International80-05B.
標題:
Applied Mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10751110
ISBN:
9780438634558
Domain Adaptation through Optimal Transport with Class Imbalance.
Gao, Keyue.
Domain Adaptation through Optimal Transport with Class Imbalance.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 71 p.
Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Thesis (Ph.D.)--New York University, 2018.
This item must not be sold to any third party vendors.
In modern data analytics, one often faces situations where the training data used to build a model and the target data to which the model is applied have different but related probability distributions. The problem of learning effectively in such scenarios is known as domain adaptation. In this paper, we propose a new framework to solve the domain adaptation problem based on optimal transport. Our approach removes two assumptions underlying prior related work. First, we allow class imbalance: that the class ratios in the training and the target data be different. Second, we allow that only a subdomain of the training data be matched to the target data, a relaxation that we name optimal partial transport. We introduce various techniques in optimal transport to address these two situations. We also present a novel regularization method which promotes small variance within each class. Numerical experiments are conducted on both synthetic and real world data, showing that our approach is effective and robust with fewer assumptions made on data than with conventional methods.
ISBN: 9780438634558Subjects--Topical Terms:
1669109
Applied Mathematics.
Domain Adaptation through Optimal Transport with Class Imbalance.
LDR
:02137nmm a2200325 4500
001
2209605
005
20191104073749.5
008
201008s2018 ||||||||||||||||| ||eng d
020
$a
9780438634558
035
$a
(MiAaPQ)AAI10751110
035
$a
(MiAaPQ)nyu:13303
035
$a
AAI10751110
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Gao, Keyue.
$3
3436702
245
1 0
$a
Domain Adaptation through Optimal Transport with Class Imbalance.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2018
300
$a
71 p.
500
$a
Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
500
$a
Publisher info.: Dissertation/Thesis.
500
$a
Advisor: Tabak, Esteban.
502
$a
Thesis (Ph.D.)--New York University, 2018.
506
$a
This item must not be sold to any third party vendors.
520
$a
In modern data analytics, one often faces situations where the training data used to build a model and the target data to which the model is applied have different but related probability distributions. The problem of learning effectively in such scenarios is known as domain adaptation. In this paper, we propose a new framework to solve the domain adaptation problem based on optimal transport. Our approach removes two assumptions underlying prior related work. First, we allow class imbalance: that the class ratios in the training and the target data be different. Second, we allow that only a subdomain of the training data be matched to the target data, a relaxation that we name optimal partial transport. We introduce various techniques in optimal transport to address these two situations. We also present a novel regularization method which promotes small variance within each class. Numerical experiments are conducted on both synthetic and real world data, showing that our approach is effective and robust with fewer assumptions made on data than with conventional methods.
590
$a
School code: 0146.
650
4
$a
Applied Mathematics.
$3
1669109
650
4
$a
Mathematics.
$3
515831
690
$a
0364
690
$a
0405
710
2
$a
New York University.
$b
Mathematics.
$3
1019424
773
0
$t
Dissertations Abstracts International
$g
80-05B.
790
$a
0146
791
$a
Ph.D.
792
$a
2018
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10751110
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9386154
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入