Number of found documents: 1586
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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

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

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

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

Neural Networks Between Integer and Rational Weights
Šíma, Jiří
2016 - English
The analysis of the computational power of neural networks with the weight parameters between integer and rational numbers is refined. We study an intermediate model of binary-state neural networks with integer weights, corresponding to finite automata, which is extended with an extra analog unit with rational weights, as already two additional analog units allow for Turing universality. We characterize the languages that are accepted by this model in terms of so-called cut languages which are combined in a certain way by usual string operations. We employ this characterization for proving that the languages accepted by neural networks with an analog unit are context-sensitive and we present an explicit example of such non-context-free languages. In addition, we formulate a sufficient condition when these networks accept only regular languages in terms of quasi-periodicity of parameters derived from their weights. Keywords: neural networks; analog unit; rational weight; cut languages; computational power Available in digital repository of the ASCR
Neural Networks Between Integer and Rational Weights

The analysis of the computational power of neural networks with the weight parameters between integer and rational numbers is refined. We study an intermediate model of binary-state neural networks ...

Šíma, Jiří
Ústav informatiky, 2016

Principy statistického uvažování
Kalina, Jan
2016 - Czech
Available in digital repository of the ASCR
Principy statistického uvažování

Kalina, Jan
Ústav informatiky, 2016

New Quasi-Newton Method for Solving Systems of Nonlinear Equations
Lukšan, Ladislav; Vlček, Jan
2016 - English
Keywords: nonlinear equations; systems of equations; trust-region methods; quasi-Newton methods; adjoint Broyden methods; numerical algorithms; numerical experiments Available in digital repository of the ASCR
New Quasi-Newton Method for Solving Systems of Nonlinear Equations

Lukšan, Ladislav; Vlček, Jan
Ústav informatiky, 2016

On Nominal Automata as Models of Java-like Object-Oriented Programs
Suzuki, Tomoyuki
2016 - English
In this paper, we proposed a model of Java-like object-oriented programs as nominal automata and a simple method invocation checker. Available on request at various institutes of the ASCR
On Nominal Automata as Models of Java-like Object-Oriented Programs

In this paper, we proposed a model of Java-like object-oriented programs as nominal automata and a simple method invocation checker.

Suzuki, Tomoyuki
Ústav informatiky, 2016

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