About: Towards Learning Hierarchical Compositional Models in the Presence of Clutter

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Our goal is to identify hierarchical compositional models from highly cluttered data. The data to learn from are assumed to be imperfect in two respects. Firstly, large portion of the data is coming from background clutter. Secondly, data generated by a recursive compositional model are subject to random replacements of correct descendants by randomly chosen ones at every level of the hierarchy. In this paper, we study the limits and capabilities of an approach which is based on likelihood maximization. The algorithm makes explicit probabilistic assignments of individual data to compositional model and background clutter. It uses these assignments to effectively focus on the data coming from the compositional model and iteratively estimate their compositional structure. Our goal is to identify hierarchical compositional models from highly cluttered data. The data to learn from are assumed to be imperfect in two respects. Firstly, large portion of the data is coming from background clutter. Secondly, data generated by a recursive compositional model are subject to random replacements of correct descendants by randomly chosen ones at every level of the hierarchy. In this paper, we study the limits and capabilities of an approach which is based on likelihood maximization. The algorithm makes explicit probabilistic assignments of individual data to compositional model and background clutter. It uses these assignments to effectively focus on the data coming from the compositional model and iteratively estimate their compositional structure. (en)
Title	Towards Learning Hierarchical Compositional Models in the Presence of Clutter Towards Learning Hierarchical Compositional Models in the Presence of Clutter (en)
skos:prefLabel	Towards Learning Hierarchical Compositional Models in the Presence of Clutter Towards Learning Hierarchical Compositional Models in the Presence of Clutter (en)
skos:notation	RIV/68407700:21230/13:00212542!RIV14-GA0-21230___
http://linked.open...avai/predkladatel	Fakulta elektrotechnická
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GAP103/12/1578)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Mačák, Jan Drbohlav, Ondřej
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	111381
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00212542
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	graphical models; unsupervised learning (en)
http://linked.open.../riv/klicoveSlovo	unsupervised learning graphical models
http://linked.open...ontrolniKodProRIV	[E74744304E14]
http://linked.open...v/mistoKonaniAkce	Naples
http://linked.open...i/riv/mistoVydani	Heidelberg
http://linked.open...i/riv/nazevZdroje	Image Analysis and Processing - ICIAP 2013
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Structural and Semantic Modeling of Architecture as a Digital Image Interpretation Problem
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Drbohlav, Ondřej Mačák, Jan
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-09-09 (xsd:date)
issn	0302-9743
number of pages	10 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-41181-6_54
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-41180-9
http://localhost/t...ganizacniJednotka	21230

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