Consistent and Tractable Algorithm for Markov Network Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00365903" target="_blank" >RIV/68407700:21230/23:00365903 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-26412-2_27" target="_blank" >https://doi.org/10.1007/978-3-031-26412-2_27</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-26412-2_27" target="_blank" >10.1007/978-3-031-26412-2_27</a>
Alternative languages
Result language
angličtina
Original language name
Consistent and Tractable Algorithm for Markov Network Learning
Original language description
Markov network (MN) structured output classifiers provide a transparent and powerful way to model dependencies between output labels. The MN classifiers can be learned using the M3N algorithm, which, however, is not statistically consistent and requires expensive fully annotated examples. We propose an algorithm to learn MN classifiers that is based on Fisher-consistent adversarial loss minimization. Learning is transformed into a tractable convex optimization that is amenable to standard gradient methods. We also extend the algorithm to learn from examples with missing labels. We show that the extended algorithm remains convex, tractable, and statistically consistent.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases, Part IV
ISBN
978-3-031-26411-5
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
17
Pages from-to
435-451
Publisher name
Springer
Place of publication
Cham
Event location
Grenoble
Event date
Sep 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000999043700027