Number of found documents: 1580
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Principy mnohorozměrného statistického uvažování
Kalina, Jan
2016 - Czech
Available on request at various institutes of the ASCR
Principy mnohorozměrného statistického uvažování

Kalina, Jan
Ústav informatiky, 2016

Nondeterministic Computations for Which Space is More Powerful than Time
Wiedermann, Jiří
2016 - English
Keywords: nondeterministic computation; crossing sequences; complexity Available in a digital repository NRGL
Nondeterministic Computations for Which Space is More Powerful than Time

Wiedermann, Jiří
Ústav informatiky, 2016

Maximum Likelihood Estimation of Diagonal Covariance Matrix
Turčičová, Marie; Mandel, Jan; Eben, Kryštof
2016 - English
Keywords: maximum likelihood estimation; parametric model; Fisher information; delta method Available in a digital repository NRGL
Maximum Likelihood Estimation of Diagonal Covariance Matrix

Turčičová, Marie; Mandel, Jan; Eben, Kryštof
Ústav informatiky, 2016

Cut Languages in Rational Bases
Šíma, Jiří; Savický, Petr
2016 - English
We introduce a so-called cut language which contains the representations of numbers in a rational base that are less than a given threshold. The cut languages can be used to refine the analysis of neural net models between integer and rational weights. We prove a necessary and sufficient condition when a cut language is regular, which is based on the concept of a quasi-periodic power series. We show that any cut language with a rational threshold is context-sensitive while examples of non-context-free cut languages are presented. Keywords: cut language; rational base; quassi-periodic power series Available in digital repository of the ASCR
Cut Languages in Rational Bases

We introduce a so-called cut language which contains the representations of numbers in a rational base that are less than a given threshold. The cut languages can be used to refine the analysis of ...

Šíma, Jiří; Savický, Petr
Ústav informatiky, 2016

Popis TDD modelu verze 3.71
Chytil, Michal; Novák, J.; Jiřina jr., M.; Benešová, M.
2016 - Czech
Zpráva je závěrečnou roční zprávou pro rok 2016 v rámci Projektu TDD-ČR. Cílem je předat metodiky pro užití modelu jak provozovatelem distribuční soustavy, tak operátorem trhu a dále informovat o aktuálním stavu modelu. Jsou popsány předávané soubory včetně vzorového výpočtu na reálných datech a jejich obsah. Keywords: typový diagram dodávky; TDD; spotřeba plynu; popis modelu Available on request at various institutes of the ASCR
Popis TDD modelu verze 3.71

Zpráva je závěrečnou roční zprávou pro rok 2016 v rámci Projektu TDD-ČR. Cílem je předat metodiky pro užití modelu jak provozovatelem distribuční soustavy, tak operátorem trhu a dále informovat o ...

Chytil, Michal; Novák, J.; Jiřina jr., M.; Benešová, M.
Ústav informatiky, 2016

Přehled metod strojového učení
Kalina, Jan
2016 - Czech
Available on request at various institutes of the ASCR
Přehled metod strojového učení

Kalina, Jan
Ústav informatiky, 2016

Some Robust Distances for Multivariate Data
Kalina, Jan; Peštová, Barbora
2016 - English
Numerous methods of multivariate statistics and data mining suffer from the presence of outlying measurements in the data. This paper presents new distance measures suitable for continuous data. First, we consider a Mahalanobis distance suitable for high-dimensional data with the number of variables (largely) exceeding the number of observations. We propose its doubly regularized version, which combines a regularization of the covariance matrix with replacing the means of multivariate data by their regularized counterparts. We formulate explicit expressions for some versions of the regularization of the means, which can be interpreted as a denoising (i.e. robust version) of standard means. Further, we propose a robust cosine similarity measure, which is based on implicit weighting of individual observations. We derive properties of the newly proposed robust cosine similarity, which includes a proof of the high robustness in terms of the breakdown point. Keywords: multivariate data; distance measures; regularization; robustness; high dimension Available on request at various institutes of the ASCR
Some Robust Distances for Multivariate Data

Numerous methods of multivariate statistics and data mining suffer from the presence of outlying measurements in the data. This paper presents new distance measures suitable for continuous data. ...

Kalina, Jan; Peštová, Barbora
Ústav informatiky, 2016

Interval Matrices: Regularity Yields Singularity
Rohn, Jiří
2016 - English
It is proved that regularity of an interval matrix implies singularity of two related interval matrices. Keywords: interval matrix; regularity; singularity Available in a digital repository NRGL
Interval Matrices: Regularity Yields Singularity

It is proved that regularity of an interval matrix implies singularity of two related interval matrices.

Rohn, Jiří
Ústav informatiky, 2016

Robust Regularized Discriminant Analysis Based on Implicit Weighting
Kalina, Jan; Hlinka, Jaroslav
2016 - English
In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables exceeding the number of observations. However, its various available versions are too vulnerable to the presence of outlying measurements in the data. In this paper, we exploit principles of robust statistics to propose new versions of regularized linear discriminant analysis suitable for highdimensional data contaminated by (more or less) severe outliers. The work exploits a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. The performance of the novel classification methods is illustrated on real data sets with a detailed analysis of data from brain activity research. Keywords: high-dimensional data; classification analysis; robustness; outliers; regularization Available at various institutes of the ASCR
Robust Regularized Discriminant Analysis Based on Implicit Weighting

In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables ...

Kalina, Jan; Hlinka, Jaroslav
Ústav informatiky, 2016

On Exact Heteroscedasticity Testing for Robust Regression
Kalina, Jan; Peštová, Barbora
2016 - English
The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also the asymptotic behavior of the permutation test statistics of the Goldfeld-Quandt and Breusch-Pagan tests is investigated. A numerical experiment on real economic data is presented, which also shows how to perform a robust prediction model under heteroscedasticity. Keywords: robust estimation; outliers; variance; diagnostic tools; heteroscedasticity Available on request at various institutes of the ASCR
On Exact Heteroscedasticity Testing for Robust Regression

The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also ...

Kalina, Jan; Peštová, Barbora
Ústav informatiky, 2016

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