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Statistical formulation of structured output learning from partially annotated examples

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00211711" target="_blank" >RIV/68407700:21230/13:00211711 - isvavai.cz</a>

  • Result on the web

    <a href="http://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Poster2013.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Poster2013.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Statistical formulation of structured output learning from partially annotated examples

  • Original language description

    Empirical risk minimization based methods for structured output learning have proved successful in real-life applications. A considerable deficiency of existing algorithms, like e.g. the Structured Output SVMs (SO-SVM), is the demand for fully annotatedtraining examples. Despite several recently published works trying to extend SO-SVM for learning from partially annotated examples, two crucial problems remain open: 1) an exact statistical formulation of risk minimization based learning from partially annotated examples and 2) an efficient learning algorithm. While the existing works attempted the algorithmic issues (i.e. the second problem), in this paper we tackle the first problem. In particular, we formulate learning of the structured output classifiers from partially annotated examples as an instance of the expected risk minimization problem. We show that the minimization of the expected risk is equivalent to the minimization of a partial loss which can be evaluated on partially a

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GAP202%2F12%2F2071" target="_blank" >GAP202/12/2071: Structured Statistical Models for Image Understanding</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2013

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    POSTER 2013 - 17th International Student Conference on Electrical Engineering

  • ISBN

    978-80-01-05242-6

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    1-5

  • Publisher name

    Czech Technical University

  • Place of publication

    Prague

  • Event location

    Prague

  • Event date

    May 16, 2013

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article