Learning Maximum Margin Markov Networks from examples with missing labels

Result code in 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>

Result on the web

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

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Learning Maximum Margin Markov Networks from examples with missing labels

Original language description

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.

Czech name

—

Czech description

—

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)

Project

<a href="/en/project/GA19-21198S" target="_blank" >GA19-21198S: Complex prediction models and their learning from weakly annotated data</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Article name in the collection

Asian Machine Learning Conference

ISBN

—

ISSN

2640-3498

e-ISSN

—

Number of pages

16

Pages from-to

—

Publisher name

Proceedings of Machine Learning Research

Place of publication

—

Event location

virtually

Event date

Nov 17, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—