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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

  • Czech description

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