On Learning Probabilistic Partial Lexicographic Preference Trees

Xudong Liu

doi:10.1109/ICMLA52953.2021.00051

On Learning Probabilistic Partial Lexicographic Preference Trees

School of Computing

Research output: Contribution to conference › Proceedings › peer-review

Original language	English
Pages	286-291
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA52953.2021.00051
State	Published - 2021
Event	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States Duration: Dec 13 2021 → Dec 16 2021

Conference

Conference	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/Territory	United States
City	Virtual, Online
Period	12/13/21 → 12/16/21

ASJC Scopus Subject Areas

Safety, Risk, Reliability and Quality
Health Informatics
Artificial Intelligence
Computer Science Applications

Keywords

Lexicographic Preference Trees
Preference Learning
Probabilistic Preference Models

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10.1109/ICMLA52953.2021.00051

Cite this

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title = "On Learning Probabilistic Partial Lexicographic Preference Trees",

keywords = "Lexicographic Preference Trees, Preference Learning, Probabilistic Preference Models",

author = "Xudong Liu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 ; Conference date: 13-12-2021 Through 16-12-2021",

year = "2021",

doi = "10.1109/ICMLA52953.2021.00051",

language = "English",

pages = "286--291",

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T1 - On Learning Probabilistic Partial Lexicographic Preference Trees

AU - Liu, Xudong

PY - 2021

Y1 - 2021

KW - Lexicographic Preference Trees

KW - Preference Learning

KW - Probabilistic Preference Models

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DO - 10.1109/ICMLA52953.2021.00051

M3 - Proceedings

AN - SCOPUS:85125877198

SP - 286

EP - 291

T2 - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Y2 - 13 December 2021 through 16 December 2021

ER -

On Learning Probabilistic Partial Lexicographic Preference Trees

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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