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

Understanding and Analyzing the Effectiveness of Uncertainty Sampling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Understanding and Analyzing the Effectiveness of Uncertainty Sampling./
作者:	Mussmann, Stephen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	112 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Active learning. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28828005
ISBN:	9798494452788

Understanding and Analyzing the Effectiveness of Uncertainty Sampling.
Mussmann, Stephen.

Understanding and Analyzing the Effectiveness of Uncertainty Sampling. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 112 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

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

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

Active learning techniques attempt to reduce the amount of data required to learn a classifier by leveraging adaptivity. In particular, an algorithm iteratively selects and labels points from an unlabeled pool of data points. Over the history of active learning, many algorithms have been developed, though one heuristic algorithm, uncertainty sampling, stands out by its popularity, effectiveness, simplicity, and intuitiveness. Despite this, uncertainty sampling has known failure modes and lacks the theoretical underpinnings of some other algorithms such as those based on disagreement. Here, we present a few analyses of uncertainty sampling. First, we find that uncertainty sampling iterations implicitly optimizes the (generally non-convex) zero-one loss, explaining how uncertainty sampling can achieve lower error than labeling the entire unlabeled pool and highlighting the importance of a good initialization. Second, for logistic regression, we show that the extent to which uncertainty sampling outperforms random sampling is inversely proportional to the asymptotic error, both theoretically and empirically. Finally, we use the previous insights to show uncertainty sampling works very well on a particular NLP task due to extreme label imbalance. Taken together, these results provide a sturdier foundation for understanding and using uncertainty sampling.

ISBN: 9798494452788Subjects--Topical Terms:

527777
Active learning.

Understanding and Analyzing the Effectiveness of Uncertainty Sampling.
LDR:02384nmm a2200301 4500 001 2349894
005 20221010063650.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798494452788
035 $a (MiAaPQ)AAI28828005
035 $a (MiAaPQ)STANFORDgw920rq6947
035 $a AAI28828005
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mussmann, Stephen. $3 3689320
245 1 0 $a Understanding and Analyzing the Effectiveness of Uncertainty Sampling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 112 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Liang, Percy;Re, Chris;Sadigh, Dorsa.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Active learning techniques attempt to reduce the amount of data required to learn a classifier by leveraging adaptivity. In particular, an algorithm iteratively selects and labels points from an unlabeled pool of data points. Over the history of active learning, many algorithms have been developed, though one heuristic algorithm, uncertainty sampling, stands out by its popularity, effectiveness, simplicity, and intuitiveness. Despite this, uncertainty sampling has known failure modes and lacks the theoretical underpinnings of some other algorithms such as those based on disagreement. Here, we present a few analyses of uncertainty sampling. First, we find that uncertainty sampling iterations implicitly optimizes the (generally non-convex) zero-one loss, explaining how uncertainty sampling can achieve lower error than labeling the entire unlabeled pool and highlighting the importance of a good initialization. Second, for logistic regression, we show that the extent to which uncertainty sampling outperforms random sampling is inversely proportional to the asymptotic error, both theoretically and empirically. Finally, we use the previous insights to show uncertainty sampling works very well on a particular NLP task due to extreme label imbalance. Taken together, these results provide a sturdier foundation for understanding and using uncertainty sampling.
590 $a School code: 0212.
650 4 $a Active learning. $3 527777
650 4 $a Artificial intelligence. $3 516317
690 $a 0800
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28828005