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Wyszukujesz frazę "reproducing kernel Hilbert space" wg kryterium: Temat


Tytuł:
Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method
Autorzy:
Fateh, Rachid
Darif, Anouar
Safi, Said
Powiązania:
https://bibliotekanauki.pl/articles/2058482.pdf
Data publikacji:
2021
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
BRAN channels
equalization
kernel method
MC-CDMA
reproducing kernel Hilbert space
Opis:
Among all the techniques combining multi-carrier modulation and spread spectrum, the multi-carrier code division multiple access (MC-CDMA) system is by far the most widely studied. In this paper, we present the performance of the MC-CDMA system associated with key single-user detection techniques. We are interested in problems related to identification and equalization of mobile radio channels, using the kernel method in Hilbert space with a reproducing kernel, and a linear adaptive algorithm, for MC-CDMA systems. In this context, we tested the efficiency of these algorithms, considering practical frequency selective fading channels, called broadband radio access network (BRAN), standardized for MC-CDMA systems. As far as the equalization problem encountered after channel identification is concerned, we use the orthogonality restoration combination (ORC) and the minimum mean square error (MMSE) equalizer techniques to correct the distortion of the channel. Simulation results demonstrate that the kernel algorithm is efficient for practical channel.
Źródło:
Journal of Telecommunications and Information Technology; 2021, 4; 1--11
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Methods of Representation for Kernel Canonical Correlation Analysis
Autorzy:
Krzyśko, Mirosław
Waszak, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/465909.pdf
Data publikacji:
2012
Wydawca:
Główny Urząd Statystyczny
Tematy:
Canonical correlation analysis generalized eigenvalue problem reproducing kernel Hilbert space
Opis:
Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation. This problem is equivalent to solving the generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we construct nonlinear canonical correlation analysis in reproducing kernel Hilbert spaces. The new kernel generalized eigenvalue problem always has the solution equal to one, and this is a typical case of over-fitting. We present methods to solve this problem and compare the results obtained by classical and kernel canonical correlation analysis.
Źródło:
Statistics in Transition new series; 2012, 13, 2; 301-310
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the Djrbashian kernel of a Siegel domain
Autorzy:
Barletta, Elisabetta
Dragomir, Sorin
Powiązania:
https://bibliotekanauki.pl/articles/1218823.pdf
Data publikacji:
1998
Wydawca:
Polska Akademia Nauk. Instytut Matematyczny PAN
Tematy:
γ-Bergman kernel
reproducing kernel Hilbert space
Djrbashian kernel
transition probability amplitude
Genchev transform
Opis:
We establish an inversion formula for the M. M. Djrbashian & A. H. Karapetyan integral transform (cf. [6]) on the Siegel domain $Ω_n = {ζ ∈ ℂ^n : ϱ (ζ) >0} $, $ϱ(ζ) = Im(ζ_1) - |ζ'|^2$. We build a family of Kähler metrics of constant holomorphic curvature whose potentials are the $ϱ^α$-Bergman kernels, α > -1, (in the sense of Z. Pasternak-Winiarski [20] of $Ω_n$. We build an anti-holomorphic embedding of $Ω_n$ in the complex projective Hilbert space $ℂℙ(H^2_α(Ω_n))$ and study (in connection with work by A. Odzijewicz [18] the corresponding transition probability amplitudes. The Genchev transform (cf. [9]) is shown to be well defined on $L^2(Ω, ϱ^α)$, for any strip Ω ⊂ ℂ, and applied in a problem of approximation by holomorphic functions. Building on work by T. Mazur (cf. [15]) we prove the existence of a complete orthonormal system in $H^2_α(Ω_n)$ consisting of eigenfunctions of a certain explicitly defined operator $V_a$, $a ∈ B_n$.
Źródło:
Studia Mathematica; 1998, 127, 1; 47-63
0039-3223
Pojawia się w:
Studia Mathematica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Functional models for Nevanlinna families
Autorzy:
Behrndt, J.
Hassi, S.
Snoo, H. de
Powiązania:
https://bibliotekanauki.pl/articles/255065.pdf
Data publikacji:
2008
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
symmetric operator
selfadjoint extension
boundary relation
Weyl family
functional model
reproducing kernel Hilbert space
Opis:
The class of Nevanlinna families consists of R-symmetric holomorphic multivalued functions on C \ R with maximal dissipative (maximal accumulative) values on C+ (C-, respectively) and is a generalization of the class of operator-valued Nevanlinna functions. In this note Nevanlinna families are realized as Weyl families of boundary relations induced by multiplication operators with the independent variable in reproducing kernel Hilbert spaces.
Źródło:
Opuscula Mathematica; 2008, 28, 3; 233-245
1232-9274
2300-6919
Pojawia się w:
Opuscula Mathematica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Conditional mean embedding and optimal feature selection via positive definite kernels
Autorzy:
Jorgensen, Palle E.T.
Song, Myung-Sin
Tiang, James
Powiązania:
https://bibliotekanauki.pl/articles/29519641.pdf
Data publikacji:
2024
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
positive-definite kernels
reproducing kernel Hilbert space
stochastic processes
frames
machine learning
embedding problem
optimization
Opis:
Motivated by applications, we consider new operator-theoretic approaches to conditional mean embedding (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive learning algorithms. For initially given non-linear data, we consider optimization-based feature selections. This entails the use of convex sets of kernels in a construction of optimal feature selection via regression algorithms from learning models. Thus, with initial inputs of training data (for a suitable learning algorithm), each choice of a kernel K in turn yields a variety of Hilbert spaces and realizations of features. A novel aspect of our work is the inclusion of a secondary optimization process over a specified convex set of positive definite kernels, resulting in the determination of “optimal” feature representations.
Źródło:
Opuscula Mathematica; 2024, 44, 1; 79-103
1232-9274
2300-6919
Pojawia się w:
Opuscula Mathematica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Decomposition of Gaussian processes, and factorization of positive definite kernels
Autorzy:
Jorgensen, Palie E. T.
Tian, Feng
Powiązania:
https://bibliotekanauki.pl/articles/255819.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
reproducing kernel Hilbert space frames
generalized Ito-integration
the measurable category analysis/synthesis
interpolation
Gaussian free fields
non-uniform sampling
optimization
transform
covariance
feature space
Opis:
We establish a duality for two lactorization questions, one for general positive definite (p.d.) kernels K, and the other for Gaussian processes, say V. The latter notion, for Gaussian processes is stated via Ito-integration. Our approach to factorization for p.d. kernels is intuitively motivated by matrix factorizations, but in infinite dimensions, subtle measure theoretic issues must be addressed. Consider a given p.d. kernel K, presented as a covariance kernel for a Gaussian process V. We then give an explicit duality for these two seemingly different notions of factorization, for p.d. kernel K, vs for Gaussian process V. Our result is in the form of an explicit correspondence. It states that the analytic data which determine the variety of factorizations for K is the exact same as that which yield factorizations for V. Examples and applications are included: point-processes, sampling schemes, constructive discretization, graph-Laplacians, and boundary-value problems.
Źródło:
Opuscula Mathematica; 2019, 39, 4; 497-541
1232-9274
2300-6919
Pojawia się w:
Opuscula Mathematica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimal trend estimation in geometric asset price models
Autorzy:
Weba, Michael
Powiązania:
https://bibliotekanauki.pl/articles/729704.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
geometric asset price model
trend estimation
Wiener process
Ornstein-Uhlenbeck process
kernel reproducing Hilbert space
exogeneous shocks
compound Poisson process
Opis:
In the general geometric asset price model, the asset price P(t) at time t satisfies the relation
$P(t) = P₀ · e^{α·f(t) + σ·F(t)}$, t ∈ [0,T],
where f is a deterministic trend function, the stochastic process F describes the random fluctuations of the market, α is the trend coefficient, and σ denotes the volatility.
The paper examines the problem of optimal trend estimation by utilizing the concept of kernel reproducing Hilbert spaces. It characterizes the class of trend functions with the property that the trend coefficient can be estimated consistently. Furthermore, explicit formulae for the best linear unbiased estimator α̂ of α and representations for the variance of α̂ are derived.
The results do not require assumptions on finite-dimensional distributions and allow of jump processes as well as exogeneous shocks. .
Źródło:
Discussiones Mathematicae Probability and Statistics; 2005, 25, 1; 51-70
1509-9423
Pojawia się w:
Discussiones Mathematicae Probability and Statistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Próbkowanie sygnałów diagnostycznych. Część 1. Próbkowanie w przestrzeni Hilberta z reprodukującym jądrem Shanona
Sampling the diagnostic signals. Part 1. Sampling in the reproducing kernel Hilbert space with Shanon kernel
Autorzy:
Syroka, Z.
Powiązania:
https://bibliotekanauki.pl/articles/328197.pdf
Data publikacji:
2007
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
próbkowanie sygnałów
przestrzenie sygnałów
przestrzeń Hilberta
jądro reprodukujące
jądro Shanona
sampling signals
signals space
Hilbert space
reproducing kernel
Shanon kernel
Opis:
W pracy przedstawiono matematyczny opis sygnałów diagnostycznych przestrzeni Hilberta oraz sposób konstrukcji tej przestrzeni. Podano teorię jąder reprodukujących w zastosowaniu do próbkowania sygnałów diagnostycznych oraz zapis klasycznego twierdzenia o próbkowaniu Shanona wykorzystującego teorię jąder reprodukujących.
In this article is defined the diagnostic signals in the reproducing kernel Hilbert space and the way this space is constructed. The theory of the reproducing kernel Hilbert space and Shanon theorem in this space were given.
Źródło:
Diagnostyka; 2007, 2(42); 19-26
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Finitely additive functions in measure theory and applications
Autorzy:
Alpay, Daniel
Jorgensen, Palle
Powiązania:
https://bibliotekanauki.pl/articles/29519748.pdf
Data publikacji:
2024
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Hilbert space
reproducing kernel
probability space
Gaussian field
transforms
covariance
Itô integration
Itô calculus
generalized Brownian motion
Opis:
In this paper, we consider, and make precise, a certain extension of the Radon–Nikodym derivative operator, to functions which are additive, but not necessarily sigma-additive, on a subset of a given sigma-algebra. We give applications to probability theory; in particular, to the study of μ-Brownian motion, to stochastic calculus via generalized Itô-integrals, and their adjoints (in the form of generalized stochastic derivatives), to systems of transition probability operators indexed by families of measures μ, and to adjoints of composition operators.
Źródło:
Opuscula Mathematica; 2024, 44, 3; 323-339
1232-9274
2300-6919
Pojawia się w:
Opuscula Mathematica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Convolutions, integral transforms and integral equations by means of the theory of reproducing kernels
Autorzy:
Castro, L. P.
Saitoh, S.
Tuan, N. M.
Powiązania:
https://bibliotekanauki.pl/articles/256011.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Hilbert space
linear transform
reproducing kernel
linear mapping
convolution
norm inequality
integral equation
Tikhonov regularization
Opis:
This paper introduces a general concept of convolutions by means of the theory of reproducing kernels which turns out to be useful for several concrete examples and applications. Consequent properties are exposed (including, in particular, associated norm inequalities).
Źródło:
Opuscula Mathematica; 2012, 32, 4; 633-646
1232-9274
2300-6919
Pojawia się w:
Opuscula Mathematica
Dostawca treści:
Biblioteka Nauki
Artykuł

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