Number of found documents: 1556
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An adaptive recursive multilevel approximate inverse preconditioning: Computation of the Schur complement
Kopal, Jiří; Rozložník, Miroslav; Tůma, Miroslav
2017 - English
Available in digital repository of the ASCR
An adaptive recursive multilevel approximate inverse preconditioning: Computation of the Schur complement

Kopal, Jiří; Rozložník, Miroslav; Tůma, Miroslav
Ústav informatiky, 2017

Exact Inference In Robust Econometrics under Heteroscedasticity
Kalina, Jan; Peštová, Barbora
2017 - 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. Theoretical results may be simply extended to the context of multivariate quantiles Keywords: heteroscedasticity; robust statistics; regression; diagnostic tools; economic data Available at various institutes of the ASCR
Exact Inference In Robust Econometrics under Heteroscedasticity

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, 2017

The Computational Power of Neural Networks and Representations of Numbers in Non-Integer Bases
Šíma, Jiří
2017 - English
We briefly survey the basic concepts and results concerning the computational power of neural networks which basically depends on the information content of weight parameters. In particular, recurrent neural networks with integer, rational, and arbitrary real weights are classified within the Chomsky and finer complexity hierarchies. Then we refine the analysis between integer and rational weights by investigating an intermediate model of integer-weight neural networks with an extra analog rational-weight neuron (1ANN). We show a representation theorem which characterizes the classification problems solvable by 1ANNs, by using so-called cut languages. Our analysis reveals an interesting link to an active research field on non-standard positional numeral systems with non-integer bases. Within this framework, we introduce a new concept of quasi-periodic numbers which is used to classify the computational power of 1ANNs within the Chomsky hierarchy. Keywords: neural network; Chomsky hierarchy; beta-expansion; cut language Available at various institutes of the ASCR
The Computational Power of Neural Networks and Representations of Numbers in Non-Integer Bases

We briefly survey the basic concepts and results concerning the computational power of neural networks which basically depends on the information content of weight parameters. In particular, recurrent ...

Šíma, Jiří
Ústav informatiky, 2017

On the optimal initial conditions for an inverse problem of model parameter estimation
Matonoha, Ctirad; Papáček, Š.
2017 - English
Available in digital repository of the ASCR
On the optimal initial conditions for an inverse problem of model parameter estimation

Matonoha, Ctirad; Papáček, Š.
Ústav informatiky, 2017

The use of sequential quadratic programming for solving reachability problems
Kuřátko, Jan
2017 - English
Available in digital repository of the ASCR
The use of sequential quadratic programming for solving reachability problems

Kuřátko, Jan
Ústav informatiky, 2017

Výběr relevantních pravidel pro podporu klinického rozhodování
Kalina, Jan; Zvárová, Jana
2017 - Czech
Keywords: podpora rozhodování; mnohorozměrná statistika; extrakce pravidel; klasifikační analýza; redukce dimensionality Available on request at various institutes of the ASCR
Výběr relevantních pravidel pro podporu klinického rozhodování

Kalina, Jan; Zvárová, Jana
Ústav informatiky, 2017

Robust regression estimators: A comparison of prediction performance
Kalina, Jan; Peštová, Barbora
2017 - English
Regression represents an important methodology for solving numerous tasks of applied econometrics. This paper is devoted to robust estimators of parameters of a linear regression model, which are preferable whenever the data contain or are believed to contain outlying measurements (outliers). While various robust regression estimators are nowadays available in standard statistical packages, the question remains how to choose the most suitable regression method for a particular data set. This paper aims at comparing various regression methods on various data sets. First, the prediction performance of common robust regression estimators are compared on a set of 24 real data sets from public repositories. Further, the results are used as input for a metalearning study over 9 selected features of individual data sets. On the whole, the least trimmed squares turns out to be superior to the least squares or M-estimators in the majority of the data sets,\nwhile the process of metalearning does not succeed in a reliable prediction of the most suitable estimator for a given data set. Keywords: robust estimation; linear regression; prediction; outliers; metalearning Available at various institutes of the ASCR
Robust regression estimators: A comparison of prediction performance

Regression represents an important methodology for solving numerous tasks of applied econometrics. This paper is devoted to robust estimators of parameters of a linear regression model, which are ...

Kalina, Jan; Peštová, Barbora
Ústav informatiky, 2017

Znalostní meze (super)inteligentních systémů
Wiedermann, Jiří
2016 - Czech
V příspěvku ukážeme nový pohled na inteligenci založený na znalostním přístupu k výpočtům. Výpočty budeme chápat jako procesy, které generují znalosti nad danou znalostní doménou v rámci příslušné znalostní teorie. V tomto kontextu budeme uvažovat inteligenci jako schopnost získávat informace a transformovat je na znalosti, které jsou dále využívány pro řešení problémů. Hlavním výsledkem příspěvku je poznatek, že pokud je znalostní doména konečná a neměnná, pak lze konstruovat inteligentní systémy s tzv. samo-zlepšující se znalostní teorii, které dříve nebo později dosáhnou takový stav poznání o dané doméně, který již nelze dále kvalitativně vylepšovat. Systém tak dosáhne meze své inteligence. Based on epistemic approach to computations we present a new perspective on intelligence. Computations will be seen as processes generating knowledge over the given knowledge domain in accordance with the respective knowledge theory. In this context intelligence will be seen as an ability to gain information and transform it to knowledge used for problem solving. The main result of the paper states that as long as the epistemic domain is finite and fixed then intelligent systems with so-called self-improving theories can be designed which soon on later will reach a state of knowledge about the underlying domain which cannot be improved any further. The system will reach the limits of its own intelligence. Keywords: znalost; inteligence; inteligentní systém; znalostní teorie; knowledge; intelligence; intelligent system; epistemic theory Available at various institutes of the ASCR
Znalostní meze (super)inteligentních systémů

V příspěvku ukážeme nový pohled na inteligenci založený na znalostním přístupu k výpočtům. Výpočty budeme chápat jako procesy, které generují znalosti nad danou znalostní doménou v rámci příslušné ...

Wiedermann, Jiří
Ústav informatiky, 2016

A Block Version of the BNS Limited-Memory Variable Metric Method for Unconstrained Minimization
Vlček, Jan; Lukšan, Ladislav
2016 - English
Keywords: unconstrained minimization; block variable metric methods; limited-memory methods; the BFGS update; global convergence; numerical results Available in digital repository of the ASCR
A Block Version of the BNS Limited-Memory Variable Metric Method for Unconstrained Minimization

Vlček, Jan; Lukšan, Ladislav
Ústav informatiky, 2016

Principy mnohorozměrného statistického uvažování
Kalina, Jan
2016 - Czech
Available at various institutes of the ASCR
Principy mnohorozměrného statistického uvažování

Kalina, Jan
Ústav informatiky, 2016

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