Learning Maximum Margin Markov Networks from examples with missing labels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00353396" target="_blank" >RIV/68407700:21230/21:00353396 - isvavai.cz</a>

Výsledek na webu

<a href="https://proceedings.mlr.press/v157/franc21a.html" target="_blank" >https://proceedings.mlr.press/v157/franc21a.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Learning Maximum Margin Markov Networks from examples with missing labels

Popis výsledku v původním jazyce

Structured output classifiers based on the framework of Markov Networks provide a transparent way to model statistical dependencies between output labels. The Markov Network (MN) classifier can be efficiently learned by the maximum margin method, which however requires expensive completely annotated examples. We extend the maximum margin algorithm for learning of unrestricted MN classifiers from examples with partially missing annotation of labels. The proposed algorithm translates learning into minimization of a novel loss function which is convex, has a clear connection with the supervised margin-rescaling loss, and can be efficiently optimized by first-order methods. We demonstrate the efficacy of the proposed algorithm on a challenging structured output classification problem where it beats deep neural network models trained from a much higher number of completely annotated examples, while the proposed method used only partial annotations.

Název v anglickém jazyce

Learning Maximum Margin Markov Networks from examples with missing labels

Popis výsledku anglicky

Structured output classifiers based on the framework of Markov Networks provide a transparent way to model statistical dependencies between output labels. The Markov Network (MN) classifier can be efficiently learned by the maximum margin method, which however requires expensive completely annotated examples. We extend the maximum margin algorithm for learning of unrestricted MN classifiers from examples with partially missing annotation of labels. The proposed algorithm translates learning into minimization of a novel loss function which is convex, has a clear connection with the supervised margin-rescaling loss, and can be efficiently optimized by first-order methods. We demonstrate the efficacy of the proposed algorithm on a challenging structured output classification problem where it beats deep neural network models trained from a much higher number of completely annotated examples, while the proposed method used only partial annotations.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-21198S" target="_blank" >GA19-21198S: Složité predikční modely a jejich učení z částečně anotovaných dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Asian Machine Learning Conference

ISBN

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ISSN

2640-3498

e-ISSN

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Počet stran výsledku

16

Strana od-do

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Název nakladatele

Proceedings of Machine Learning Research

Místo vydání

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Místo konání akce

virtually

Datum konání akce

17. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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