Počet nalezených dokumentů: 669
Publikováno od do

UFO 2013 Interactive System for Universal Functional Optimization.
Lukšan, Ladislav; Tůma, Miroslav; Vlček, Jan; Ramešová, Nina; Šiška, M.; Matonoha, Ctirad; Hartman, J.
2014 - anglický
Klíčová slova: numerical optimization; nonlinear programming; nonlinear approximation; algorithms; software systems Plné texty jsou dostupné v digitálním repozitáři NUŠL
UFO 2013 Interactive System for Universal Functional Optimization.

Lukšan, Ladislav; Tůma, Miroslav; Vlček, Jan; Ramešová, Nina; Šiška, M.; Matonoha, Ctirad; Hartman, J.
Ústav informatiky, 2014

Fuzzified linear orderings, fuzzy maxima and minima
Běhounek, Libor
2014 - anglický
Klíčová slova: fuzzy relation; similarity relation; fuzzy ordering; fuzzy maximum; higher-order fuzzy logic Plné texty jsou dostupné v digitálním repozitáři NUŠL
Fuzzified linear orderings, fuzzy maxima and minima

Běhounek, Libor
Ústav informatiky, 2014

Kernel density estimates in particle filter
Coufal, David
2014 - anglický
Klíčová slova: particle filters; kernel methods; Fourier analysis Plné texty jsou dostupné v digitálním repozitáři NUŠL
Kernel density estimates in particle filter

Coufal, David
Ústav informatiky, 2014

UFO 2014. Interactive System for Universal Functional Optimization
Lukšan, Ladislav; Tůma, Miroslav; Matonoha, Ctirad; Vlček, Jan; Ramešová, Nina; Šiška, M.; Hartman, J.
2014 - anglický
Klíčová slova: numerical optimization; nonlinear programming; nonlinear approximation; algorithms; software systems Plné texty jsou dostupné v digitálním repozitáři NUŠL
UFO 2014. Interactive System for Universal Functional Optimization

Lukšan, Ladislav; Tůma, Miroslav; Matonoha, Ctirad; Vlček, Jan; Ramešová, Nina; Šiška, M.; Hartman, J.
Ústav informatiky, 2014

Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models
Chotard, A.; Holeňa, Martin
2014 - anglický
Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of the involved random steps. This is a very strong and specific assumption. The objective of our contribution is to show that in the constant step size case, valuable properties of the Markov chain can be obtained even for steps with substantially more general distributions. Several results that have been previously proved using the normality assumption are proved here in a more general way without that assumption. Finally, the decomposition of a multidimensional distribution into its marginals and the copula combining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas. Klíčová slova: evolution strategies; random steps; linear optimization; Markov chain models; Archimedean copulas Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models

Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and ...

Chotard, A.; Holeňa, Martin
Ústav informatiky, 2014

Robustness of High-Dimensional Data Mining
Kalina, Jan; Duintjer Tebbens, Jurjen; Schlenker, Anna
2014 - anglický
Standard data mining procedures are sensitive to the presence of outlying measurements in the data. This work has the aim to propose robust versions of some existing data mining procedures, i.e. methods resistant to outliers. In the area of classification analysis, we propose a new robust method based on a regularized version of the minimum weighted covariance determinant estimator. The method is suitable for data with the number of variables exceeding the number of observations. The method is based on implicit weights assigned to individual observations. Our approach is a unique attempt to combine regularization and high robustness, allowing to downweight outlying high-dimensional observations. Classification performance of new methods and some ideas concerning classification analysis of high-dimensional data are illustrated on real raw data as well as on data contaminated by severe outliers. Klíčová slova: classification analysis; robust estimation; high-dimensional data Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
Robustness of High-Dimensional Data Mining

Standard data mining procedures are sensitive to the presence of outlying measurements in the data. This work has the aim to propose robust versions of some existing data mining procedures, i.e. ...

Kalina, Jan; Duintjer Tebbens, Jurjen; Schlenker, Anna
Ústav informatiky, 2014

A Class of Explicitly Solvable Absolute Value Equations
Rohn, Jiří
2014 - anglický
Klíčová slova: absolute value equation; solution; explicit form Plné texty jsou dostupné v digitálním repozitáři NUŠL
A Class of Explicitly Solvable Absolute Value Equations

Rohn, Jiří
Ústav informatiky, 2014

Explicit Form of Matrices Qz for an Interval Matrix with Unit Midpoint
Rohn, Jiří
2014 - anglický
Klíčová slova: interval matrix; Qz matrix; unit midpoint; explicit formula Plné texty jsou dostupné v digitálním repozitáři NUŠL
Explicit Form of Matrices Qz for an Interval Matrix with Unit Midpoint

Rohn, Jiří
Ústav informatiky, 2014

A posteriori algebraic error estimation in numerical solution of linear diffusion PDEs
Papež, Jan; Vohralík, M.
2014 - anglický
Klíčová slova: finite element method; algebraic error; a posteriori error estimation; stopping criteria Plné texty jsou dostupné v digitálním repozitáři NUŠL
A posteriori algebraic error estimation in numerical solution of linear diffusion PDEs

Papež, Jan; Vohralík, M.
Ústav informatiky, 2014

A Hybrid Method for Solving Absolute Value Equations
Rohn, Jiří
2014 - anglický
Klíčová slova: absolute value equation; iterative method; hybrid method Plné texty jsou dostupné v digitálním repozitáři NUŠL
A Hybrid Method for Solving Absolute Value Equations

Rohn, Jiří
Ústav informatiky, 2014

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