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

Probabilistic Learning of Operator I...

Kravitz, Eli.

FindBook

Google Book

Amazon

博客來

Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization./
作者:	Kravitz, Eli.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	105 p.
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30425047
ISBN:	9798379696733

Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization.
Kravitz, Eli.

Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 105 p.

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of Colorado at Boulder, 2023.

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

Understanding user interests and behaviors is an important aspect to consider when developing machine learning models to augment data-intensive human workflows. By understanding what a user desires, these models can help prioritize workflow and essential information for decision making the way a human would, and form the baseline for trusted autonomous systems. This work considers the problem of human interest classification in the context of missile defense surveillance. In this case, users are satellite operators who must prioritize simultaneous processing and characterization of multiple (e.g. hundreds of) target tracks across the globe for extended periods of time under strict time and accuracy constraints.The given problem is particularly challenging because user interest is not generally static, tracks have a short lifespan, and the user pool is small. Here, the problem is formulated similarly to a binary Bayesian logistic regression problem to classify operator interest in a given candidate track, but with the added complexity of partially observable input track feature variables and dependencies between these variables. Prior feature weight distributions are developed based on human inputs, and labeled training data is generated from online user interactions with the system. Derivations for a novel probabilistic model lead to intractable conditional posterior distributions for regression feature weights. These distributions are approximated in real-time online. Given trained weight distributions, human interest for unlabeled candidate tracks is inferred.The developed interest classification model is validated using a simulated truth model by examining average track interest, learning rate, and Discounted Cumulative Gain for assessing relevancy of classified tracks. Validation is performed on several use cases, including an ideal case as well as several non-ideal cases. The results indicate that the model is robust to challenging cases, and is able to accurately learn features that interest a human operator. The introduction of a probabilistic model allowed for interest to be learned with limited prior information or labeled training data, and addressed the challenging problem of inferring user interest in a surveillance environment.

ISBN: 9798379696733Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Bayesian networks

Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization.
LDR:03552nmm a2200409 4500 001 2397187
005 20240617111342.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379696733
035 $a (MiAaPQ)AAI30425047
035 $a AAI30425047
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kravitz, Eli. $3 3766951
245 1 0 $a Probabilistic Learning of Operator Interest in Surveillance Environments for Online Track Characterization.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 105 p.
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Ahmed, Nisar.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Understanding user interests and behaviors is an important aspect to consider when developing machine learning models to augment data-intensive human workflows. By understanding what a user desires, these models can help prioritize workflow and essential information for decision making the way a human would, and form the baseline for trusted autonomous systems. This work considers the problem of human interest classification in the context of missile defense surveillance. In this case, users are satellite operators who must prioritize simultaneous processing and characterization of multiple (e.g. hundreds of) target tracks across the globe for extended periods of time under strict time and accuracy constraints.The given problem is particularly challenging because user interest is not generally static, tracks have a short lifespan, and the user pool is small. Here, the problem is formulated similarly to a binary Bayesian logistic regression problem to classify operator interest in a given candidate track, but with the added complexity of partially observable input track feature variables and dependencies between these variables. Prior feature weight distributions are developed based on human inputs, and labeled training data is generated from online user interactions with the system. Derivations for a novel probabilistic model lead to intractable conditional posterior distributions for regression feature weights. These distributions are approximated in real-time online. Given trained weight distributions, human interest for unlabeled candidate tracks is inferred.The developed interest classification model is validated using a simulated truth model by examining average track interest, learning rate, and Discounted Cumulative Gain for assessing relevancy of classified tracks. Validation is performed on several use cases, including an ideal case as well as several non-ideal cases. The results indicate that the model is robust to challenging cases, and is able to accurately learn features that interest a human operator. The introduction of a probabilistic model allowed for interest to be learned with limited prior information or labeled training data, and addressed the challenging problem of inferring user interest in a surveillance environment.
590 $a School code: 0051.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Computer science. $3 523869
650 4 $a Computational physics. $3 3343998
653 $a Bayesian networks
653 $a Data sparse learning
653 $a Machine learning
653 $a Preference elicitation
653 $a Probabilistic modeling
690 $a 0538
690 $a 0984
690 $a 0800
690 $a 0216
710 2 $a University of Colorado at Boulder. $b Aerospace Engineering. $3 1030473
773 0 $t Masters Abstracts International $g 84-12.
790 $a 0051
791 $a M.S.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30425047