About: Multi-dimensional Compound Probabilistic Models

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Projekt, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	http://linked.opendata.cz/ontology/domain/vavai/Projekt
rdfs:seeAlso	http://www.isvav.cz/projectDetail.do?rowId=IAA2075302
Description	The project deals with multi-dimensional probabilistic models and their application in several technical fields, mainly in pattern recognition and image modelling. Regarding the fact that these models, in a general from, will always be computationally intractable, the research focuses on special class of distributions,called compound models, serving as appropriate approximations. The approximating distributions are composed of several components, each of wich is defined with a reasonable number of parameters. We are distinguishing two main families of compound models: chain models (with typical representatives grtaphical Markov models) and hawser models (represented by finite mixture models). For both these subclasses of models parameter estimation, optimal (suboptimal) model selection, models synthesis along with efficient algorithms development for model construction will be proposed. The solution of these problems requires non-trivial theoretical model analysis. (en) Projekt řeší problémy aplikací mnohorozměrných pravděpodobnostních modelů ve vybraných technických oborech, především v oblasti rozpoznávání obrazů a modelování obrazové informace. Vzhledem k tomu, že tyto modely v plné obecnosti budou vždy přesahovat možnosti počítačového zpracování, výzkum se zaměří na speciální třídu aproximujících distribucí, kterou nazýváme komponentové modely. Tyto modely jsou definovány omezeným počtem parametrů. Rozlišujeme dvě podtřídy modelů: řetězcové modely (typickými příklady jsou grafické markovské modely) a lanové modely (reprezentované modely založenými na směsích). Pro obě uvažované podtřídy budou řešeny problémy týkající se odhadu parametrů, výběru optimálních (suboptimálních) modelů, syntézou modelů a návrhem efektivních algoritmů pro jejich využití. Řešení těchto problémů vyžaduje netriviální teoretickou analýzu vlastností uvažovaných modelů.
Title	Multi-dimensional Compound Probabilistic Models (en) Multidimenzionální komponentové pravděpodobnostní modely
skos:notation	IAA2075302
http://linked.open...avai/cep/aktivita	Grants of distinctly investigative character focused on the sphere of research pursued at present particularly in the Academy of Sciences of the Czech Republic
http://linked.open...kovaStatniPodpora	http://linked.opendata.cz/resource/domain/vavai/projekt/IAA2075302/celkovaStatniPodpora
http://linked.open...ep/celkoveNaklady	http://linked.opendata.cz/resource/domain/vavai/projekt/IAA2075302/celkoveNaklady
http://linked.open...datumDodatniDoRIV	2013-06-28 (xsd:date)
http://linked.open...i/cep/druhSouteze	VS - Veřejná soutěž ve výzkumu a vývoji
http://linked.open...ep/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open.../cep/fazeProjektu	71727096
http://linked.open...ai/cep/hlavniObor	BB - Aplikovaná statistika, operační výzkum
http://linked.open...hodnoceniProjektu	V - Vynikající výsledky (s mezinárodním významem apod.). Zároveň byly splněny cíle a předpokládané výsledky uvedené ve smlouvě / rozhodnutí o poskytnutí podpory.
http://linked.open...vai/cep/kategorie	ZV - Základní výzkum
http://linked.open.../cep/klicovaSlova	Probabilistic methods; Bayesian networks; mixture-based distributions; composition of distributions; soft computing; applications; pattern recognition; model synthesis (en)
http://linked.open...ep/partnetrHlavni	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...inujicichPrijemcu	0 (xsd:int)
http://linked.open...cep/pocetPrijemcu	1 (xsd:int)
http://linked.open...ocetSpoluPrijemcu	0 (xsd:int)
http://linked.open.../pocetVysledkuRIV	86 (xsd:int)
http://linked.open...enychVysledkuVRIV	86 (xsd:int)
http://linked.open...lneniVMinulemRoce	2007-02-27 (xsd:date)
http://linked.open.../prideleniPodpory	http://linked.opendata.cz/resource/domain/vavai/cep/prideleniPodpory/IAA2075302
http://linked.open...iciPoslednihoRoku	2007
http://linked.open...atUdajeProjZameru	2008
http://linked.open.../vavai/cep/soutez	SAV02003-A
http://linked.open...usZobrazovaneFaze	DUU
http://linked.open...ai/cep/typPojektu	P - Projekt výzkumu a vývoje financovaný ze státního rozpočtu
http://linked.open...ep/ukonceniReseni	2007-12-01 (xsd:date)
http://linked.open.../cep/vedlejsiObor	BD - Teorie informace JD - Využití počítačů, robotika a její aplikace
http://linked.open...ep/zahajeniReseni	2003-01-01 (xsd:date)
http://linked.open...jektu+dodavatelem	Byla odvozena řada originálních teoretických poznatků o mnohodimenzionálních efektivně reprezentovatelných pravděpodobnostních modelech. Tyto výsledky byly použity k návrhu výpočetních algoritmů a k odvození nových metod pro zpracování textů a obrázků. (cs) Within the project number of original theoretical results on multidimensional efficiently reprezentable probabilistic models were deduced. These results supported development of computational algorithms and design of image and text processing procedures. (en)
http://linked.open...tniCyklusProjektu	ZBBBKU
http://linked.open.../cep/klicoveSlovo	Bayesian networks applications composition of distributions mixture-based distributions pattern recognition soft computing Probabilistic methods
is http://linked.open...vavai/riv/projekt of	Performance Parameters Definition and Processing Decomposition of Bayesian Networks Induced by Compositional Models Data-Based Construction of Multidimensional Probabilistic Models with MUDIM Copulas and characterization of T-product possibility measures Decomposition of Bayesian networks induced by compositional models Data-based construction of multidimensional probabilistic models Probabilistic Model-Based Restoration of Short-Exposure Astronomical Images Probabilistic partial knowledge handling Are Better Feature Selection Methods Actually Better? Discussion, Reasoning and Examples Geometry of possibility measures on finite sets Unsupervised Mammograms Segmentation Persegrams of Compositional Models Revisited: conditional independence There are combinations and compositions in Dempster-Shafer theory of evidence Knowledge-based decision-making supported by compositional models Multiple Classifier Systems Are Better Feature Selection Methods Actually Better? Discussion, Reasoning and Examples. Application of finite mixtures to text document classification. Application of multinomial mixture model to text classification. Fast BTF Texture Modelling On approximating multidimensional probability distributions by compositional models. On experimental system for multidimensional model development MUDIM. A Fast Probabilistic Bidirectional Texture Function Model Feature Selection using Improved Mutual Information for Text Classification Feature selection using improved mutual information for text classification Non-linear Reflectance Model for Bidirectional Texture Function Synthesis Model-Based Texture Segmentation Compositional Model for Belief Functions Flexible-hybrid sequential floating search in statistical feature selection. Compositional Models and Maximum Entropy Principle Probabilistic suppression of astronomical image degradations Multiple Classifier Systems Validation of Classical and Blind Criteria for Image Quality Evaluation Validation of Classical and Blind Criteria for Image Quality Evaluation Restoration of Multitemporal Short-Exposure Astronomical Images Colour Texture Segmentation Using Modelling Approach A roller - fast sampling-based texture synthesis algorithm Text Document Classification Information -Theoretic Feature Selection Algorithms for Text Classification Modelling of authentic reflectance behaviour in virtual environments Efficient Image-Based Bidirectional Texture Function Model BTF Roller A Hybrid BTF Model Based on Gaussian Mixtures Novel Path Search Algorithm for Image Stitching and Advanced Texture Tiling What is the Difference between Bayesian Networks and Compositional Models What is the difference between Bayesian networks and compositional models? Information-theoretic feature selection algorithms for text classification Colour texture segmentation using modelling approach Oscillating Feature Subset Search Algorithm for Text Categorization Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering Theoretical Foundations for Marginalization in Compositional Models Prague texture segmentation data generator and benchmark Marginalization algorithm for compositional models Fast Synthesis of Dynamic Colour Textures BTF Modelling Using BRDF Texels Multimodal Range Image Segmentation by Curve Grouping

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