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Готові списки джерел за темами / Hilbert spaces

Добірка наукової літератури з теми "Hilbert spaces"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 6 вересня 2023

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Статті в журналах з теми "Hilbert spaces":

1

Sharma, Sumit Kumar, and Shashank Goel. "Frames in Quaternionic Hilbert Spaces." Zurnal matematiceskoj fiziki, analiza, geometrii 15, no. 3 (June 25, 2019): 395–411. http://dx.doi.org/10.15407/mag15.03.395.

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2

Bellomonte, Giorgia, and Camillo Trapani. "Rigged Hilbert spaces and contractive families of Hilbert spaces." Monatshefte für Mathematik 164, no. 3 (October 8, 2010): 271–85. http://dx.doi.org/10.1007/s00605-010-0249-1.

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3

Sánchez, Félix Cabello. "Twisted Hilbert spaces." Bulletin of the Australian Mathematical Society 59, no. 2 (April 1999): 177–80. http://dx.doi.org/10.1017/s0004972700032792.

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A Banach space X is called a twisted sum of the Banach spaces Y and Z if it has a subspace isomorphic to Y such that the corresponding quotient is isomorphic to Z. A twisted Hilbert space is a twisted sum of Hilbert spaces. We prove the following tongue-twister: there exists a twisted sum of two subspaces of a twisted Hilbert space that is not isomorphic to a subspace of a twisted Hilbert space. In other words, being a subspace of a twisted Hilbert space is not a three-space property.

4

CHITESCU, ION, RAZVAN-CORNEL SFETCU, and OANA COJOCARU. "Kothe-Bochner spaces that are Hilbert spaces." Carpathian Journal of Mathematics 33, no. 2 (2017): 161–68. http://dx.doi.org/10.37193/cjm.2017.02.03.

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We are concerned with Kothe-Bochner spaces that are Hilbert spaces (resp. hilbertable spaces). It is shown that ¨ this is equivalent to the fact that, separately, Lρ and X are Hilbert spaces (resp. hilbertable spaces). The complete characterization of the Lρ spaces that are Hilbert spaces, given by the first-author, is used.

5

Pisier, Gilles. "Weak Hilbert Spaces." Proceedings of the London Mathematical Society s3-56, no. 3 (May 1988): 547–79. http://dx.doi.org/10.1112/plms/s3-56.3.547.

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6

Fabian, M., G. Godefroy, P. Hájek, and V. Zizler. "Hilbert-generated spaces." Journal of Functional Analysis 200, no. 2 (June 2003): 301–23. http://dx.doi.org/10.1016/s0022-1236(03)00044-2.

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7

Rudolph, Oliver. "Super Hilbert Spaces." Communications in Mathematical Physics 214, no. 2 (November 2000): 449–67. http://dx.doi.org/10.1007/s002200000281.

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8

Ng, Chi-Keung. "Topologized Hilbert spaces." Journal of Mathematical Analysis and Applications 418, no. 1 (October 2014): 108–20. http://dx.doi.org/10.1016/j.jmaa.2014.03.073.

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9

van den Boogaart, Karl Gerald, Juan José Egozcue, and Vera Pawlowsky-Glahn. "Bayes Hilbert Spaces." Australian & New Zealand Journal of Statistics 56, no. 2 (June 2014): 171–94. http://dx.doi.org/10.1111/anzs.12074.

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10

Schmitt, L. M. "Semidiscrete Hilbert spaces." Acta Mathematica Hungarica 53, no. 1-2 (March 1989): 103–7. http://dx.doi.org/10.1007/bf02170059.

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Дисертації з теми "Hilbert spaces":

1

Wigestrand, Jan. "Inequalities in Hilbert Spaces." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9673.

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The main result in this thesis is a new generalization of Selberg's inequality in Hilbert spaces with a proof. In Chapter 1 we define Hilbert spaces and give a proof of the Cauchy-Schwarz inequality and the Bessel inequality. As an example of application of the Cauchy-Schwarz inequality and the Bessel inequality, we give an estimate for the dimension of an eigenspace of an integral operator. Next we give a proof of Selberg's inequality including the equality conditions following [Furuta]. In Chapter 2 we give selected facts on positive semidefinite matrices with proofs or references. Then we use this theory for positive semidefinite matrices to study inequalities. First we give a proof of a generalized Bessel inequality following [Akhiezer,Glazman], then we use the same technique to give a new proof of Selberg's inequality. We conclude with a new generalization of Selberg's inequality with a proof. In the last section of Chapter 2 we show how the matrix approach developed in Chapter 2.1 and Chapter 2.2 can be used to obtain optimal frame bounds. We introduce a new notation for frame bounds.

2

Ameur, Yacin. "Interpolation of Hilbert spaces." Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-1753.

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(i) We prove that intermediate Banach spaces A, B with respect to arbitrary Hilbert couples H, K are exact interpolation iff they are exact K-monotonic, i.e. the condition f0∊A and the inequality K(t,g0;K)≤K(t,f0;H), t>0 imply g0∊B and ||g0||B≤||f0||A (K is Peetre's K-functional). It is well-known that this property is implied by the following: for each ρ>1 there exists an operator T : H→K such that Tf0=g0, and K(t,Tf;K)≤ρK(t,f;H), f∊H0+H1, t>0.Verifying the latter property, it suffices to consider the "diagonal" case where H=K is finite-dimensional. In this case, we construct the relevant operators by a method which allows us to explicitly calculate them. In the strongest form of the theorem, it is shown that the statement remains valid when substituting ρ=1. (ii) A new proof is given to a theorem of W. F. Donoghue which characterizes certain classes of functions whose domain of definition are finite sets, and which are subject to certain matrix inequalities. The result generalizes the classical Löwner theorem on monotone matrix functions, and also yields some information with respect to the finer study of monotone functions of finite order. (iii) It is shown that with respect to a positive concave function ψ there exists a function h, positive and regular on ℝ+ and admitting of analytic continuation to the upper half-plane and having positive imaginary part there, such that h≤ψ≤ 2h. This fact is closely related to a theorem of Foiaş, Ong and Rosenthal, which states that regardless of the choice of a concave function ψ, and a weight λ, the weighted l2-space l2(ψ(λ)) is c-interpolation with respect to the couple (l2,l2(λ)), where we have c≤√2 for the best c. It turns out that c=√2 is best possible in this theorem; a fact which is implicit in the work of G. Sparr. (iv) We give a new proof and new interpretation (based on the work (ii) above) of Donoghue's interpolation theorem; for an intermediate Hilbert space H* to be exact interpolation with respect to a regular Hilbert couple H it is necessary and sufficient that the norm in H* be representable in the form ||f||*= (∫[0,∞] (1+t-1)K2(t,f;H)2dρ(t))1/2 with some positive Radon measure ρ on the compactified half-line [0,∞]. (v) The theorem of W. F. Donoghue (item (ii) above) is extended to interpolation of tensor products. Our result is related to A. Korányi's work on monotone matrix functions of several variables.

3

Ameur, Yacin. "Interpolation of Hilbert spaces /." Uppsala : Matematiska institutionen, Univ. [distributör], 2001. http://publications.uu.se/theses/91-506-1531-9/.

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4

Panayotov, Ivo. "Conjugate gradient in Hilbert spaces." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82402.

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In this thesis, we examine the Conjugate Gradient algorithm for solving self-adjoint positive definite linear systems in Cn . We generalize the algorithm by proving a convergence result of Conjugate Gradient for self-adjoint positive definite operators in an arbitrary Hilbert space H. Then, we use the Maple software for symbolic manipulation to implement a general version of Conjugate Gradient and to demonstrate, by examples, that the algorithm can be used directly to solve problems in Hilbert spaces other than Cn .

5

Bahmani, Fatemeh. "Ternary structures in Hilbert spaces." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/697.

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Ternary structures in Hilbert spaces arose in the study of in nite dimensional manifolds in di erential geometry. In this thesis, we develop a structure theory of Hilbert ternary algebras and Jordan Hilbert triples which are Hilbert spaces equipped with a ternary product. We obtain several new results on the classi - cation of these structures. Some results have been published in [2]. A Hilbert ternary algebra is a real Hilbert space (V; h ; i) equipped with a ternary product [ ; ; ] satisfying h[a; b; x]; yi = hx; [b; a; y]i for a; b; x and y in V . A Jordan Hilbert triple is a real Hilbert space in which the ternary product f ; ; g is a Jordan triple product. It is called a JH-triple if the identity hfa; b; xg; xi = hx; fb; a; xgi holds in V . JH-triples correspond to a class of Lie algebras which play an important role in symmetric Riemannian manifolds. We begin by proving new structure results on ideals, centralizers and derivations of Hilbert ternary algebras. We characterize primitive tripotents in Hilbert ternary algebras and use them as coordinates to classify abelian Hilbert ternary algebras. We show that they are direct sums of simple ones, and each simple abelian Hilbert ternary algebra is ternary isomorphic to the algebra C2(H;K) of Hilbert-Schmidt operators between real, complex or quaternion Hilbert spaces H and K. Further, we describe completely the ternary isomorphisms and ternary antiisomorphisms between abelian Hilbert ternary algebras. We show that each ternary isomorphism between simple algebras C2(H;K) and C2(H0;K0) is of the form (x) = Jxj where j : H0 ! H and J : K ! K0 are isometries. A ternary anti-isomorphism is of the form (x) = Jx j where j : H0 ! K and J : H ! K0 are isometries. The structures of Hilbert ternary algebras and JH-triples are closely related. Indeed, we show that each JH-triple (V; f ; ; g) admits a decomposi- 6 tion V = Vs L V ? s where (Vs; f ; ; g) is a Hilbert ternary algebra which is usually nonabelian and unless V = Vs, the orthogonal complement V ? s is always a nonabelian Hilbert ternary algebra in the derived ternary product [a; b; c]d = fa; b; cg fb; a; cg. Hence JH-triples provide important examples of nonabelian Hilbert ternary algebras. We determine exactly when Vs and V ? s are Jordan triple ideals of V . We show, in each dimension at least 2, there is a JH-triple (V; f ; ; g) for which V 6= Vs, equivalently, (V; f ; ; g) is not a Hilbert ternary algebra. 7

6

Das, Tushar. "Kleinian Groups in Hilbert Spaces." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149579/.

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The theory of discrete groups acting on finite dimensional Euclidean open balls by hyperbolic isometries was borne around the end of 19th century within the works of Fuchs, Klein and Poincaré. We develop the theory of discrete groups acting by hyperbolic isometries on the open unit ball of an infinite dimensional separable Hilbert space. We present our investigations on the geometry of limit sets at the sphere at infinity with an attempt to highlight the differences between the finite and infinite dimensional theories. We discuss the existence of fixed points of isometries and the classification of isometries. Various notions of discreteness that were equivalent in finite dimensions, no longer turn out to be in our setting. In this regard, the robust notion of strong discreteness is introduced and we study limit sets for properly discontinuous actions. We go on to prove a generalization of the Bishop-Jones formula for strongly discrete groups, equating the Hausdorff dimension of the radial limit set with the Poincaré exponent of the group. We end with a short discussion on conformal measures and their relation with Hausdorff and packing measures on the limit set.

7

Harris, Terri Joan Mrs. "HILBERT SPACES AND FOURIER SERIES." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/244.

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I give an overview of the basic theory of Hilbert spaces necessary to understand the convergence of the Fourier series for square integrable functions. I state the necessary theorems and definitions to understand the formulations of the problem in a Hilbert space framework, and then I give some applications of the theory along the way.

8

Dieuleveut, Aymeric. "Stochastic approximation in Hilbert spaces." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE059/document.

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Le but de l’apprentissage supervisé est d’inférer des relations entre un phénomène que l’on souhaite prédire et des variables « explicatives ». À cette fin, on dispose d’observations de multiples réalisations du phénomène, à partir desquelles on propose une règle de prédiction. L’émergence récente de sources de données à très grande échelle, tant par le nombre d’observations effectuées (en analyse d’image, par exemple) que par le grand nombre de variables explicatives (en génétique), a fait émerger deux difficultés : d’une part, il devient difficile d’éviter l’écueil du sur-apprentissage lorsque le nombre de variables explicatives est très supérieur au nombre d’observations; d’autre part, l’aspect algorithmique devient déterminant, car la seule résolution d’un système linéaire dans les espaces en jeupeut devenir une difficulté majeure. Des algorithmes issus des méthodes d’approximation stochastique proposent uneréponse simultanée à ces deux difficultés : l’utilisation d’une méthode stochastique réduit drastiquement le coût algorithmique, sans dégrader la qualité de la règle de prédiction proposée, en évitant naturellement le sur-apprentissage. En particulier, le cœur de cette thèse portera sur les méthodes de gradient stochastique. Les très populaires méthodes paramétriques proposent comme prédictions des fonctions linéaires d’un ensemble choisi de variables explicatives. Cependant, ces méthodes aboutissent souvent à une approximation imprécise de la structure statistique sous-jacente. Dans le cadre non-paramétrique, qui est un des thèmes centraux de cette thèse, la restriction aux prédicteurs linéaires est levée. La classe de fonctions dans laquelle le prédicteur est construit dépend elle-même des observations. En pratique, les méthodes non-paramétriques sont cruciales pour diverses applications, en particulier pour l’analyse de données non vectorielles, qui peuvent être associées à un vecteur dans un espace fonctionnel via l’utilisation d’un noyau défini positif. Cela autorise l’utilisation d’algorithmes associés à des données vectorielles, mais exige une compréhension de ces algorithmes dans l’espace non-paramétrique associé : l’espace à noyau reproduisant. Par ailleurs, l’analyse de l’estimation non-paramétrique fournit également un éclairage révélateur sur le cadre paramétrique, lorsque le nombre de prédicteurs surpasse largement le nombre d’observations. La première contribution de cette thèse consiste en une analyse détaillée de l’approximation stochastique dans le cadre non-paramétrique, en particulier dans le cadre des espaces à noyaux reproduisants. Cette analyse permet d’obtenir des taux de convergence optimaux pour l’algorithme de descente de gradient stochastique moyennée. L’analyse proposée s’applique à de nombreux cadres, et une attention particulière est portée à l’utilisation d’hypothèses minimales, ainsi qu’à l’étude des cadres où le nombre d’observations est connu à l’avance, ou peut évoluer. La seconde contribution est de proposer un algorithme, basé sur un principe d’accélération, qui converge à une vitesse optimale, tant du point de vue de l’optimisation que du point de vue statistique. Cela permet, dans le cadre non-paramétrique, d’améliorer la convergence jusqu’au taux optimal, dans certains régimes pour lesquels le premier algorithme analysé restait sous-optimal. Enfin, la troisième contribution de la thèse consiste en l’extension du cadre étudié au delà de la perte des moindres carrés : l’algorithme de descente de gradient stochastiqueest analysé comme une chaine de Markov. Cette approche résulte en une interprétation intuitive, et souligne les différences entre le cadre quadratique et le cadre général. Une méthode simple permettant d’améliorer substantiellement la convergence est également proposée
The goal of supervised machine learning is to infer relationships between a phenomenon one seeks to predict and “explanatory” variables. To that end, multiple occurrences of the phenomenon are observed, from which a prediction rule is constructed. The last two decades have witnessed the apparition of very large data-sets, both in terms of the number of observations (e.g., in image analysis) and in terms of the number of explanatory variables (e.g., in genetics). This has raised two challenges: first, avoiding the pitfall of over-fitting, especially when the number of explanatory variables is much higher than the number of observations; and second, dealing with the computational constraints, such as when the mere resolution of a linear system becomes a difficulty of its own. Algorithms that take their roots in stochastic approximation methods tackle both of these difficulties simultaneously: these stochastic methods dramatically reduce the computational cost, without degrading the quality of the proposed prediction rule, and they can naturally avoid over-fitting. As a consequence, the core of this thesis will be the study of stochastic gradient methods. The popular parametric methods give predictors which are linear functions of a set ofexplanatory variables. However, they often result in an imprecise approximation of the underlying statistical structure. In the non-parametric setting, which is paramount in this thesis, this restriction is lifted. The class of functions from which the predictor is proposed depends on the observations. In practice, these methods have multiple purposes, and are essential for learning with non-vectorial data, which can be mapped onto a vector in a functional space using a positive definite kernel. This allows to use algorithms designed for vectorial data, but requires the analysis to be made in the non-parametric associated space: the reproducing kernel Hilbert space. Moreover, the analysis of non-parametric regression also sheds some light on the parametric setting when the number of predictors is much larger than the number of observations. The first contribution of this thesis is to provide a detailed analysis of stochastic approximation in the non-parametric setting, precisely in reproducing kernel Hilbert spaces. This analysis proves optimal convergence rates for the averaged stochastic gradient descent algorithm. As we take special care in using minimal assumptions, it applies to numerous situations, and covers both the settings in which the number of observations is known a priori, and situations in which the learning algorithm works in an on-line fashion. The second contribution is an algorithm based on acceleration, which converges at optimal speed, both from the optimization point of view and from the statistical one. In the non-parametric setting, this can improve the convergence rate up to optimality, even inparticular regimes for which the first algorithm remains sub-optimal. Finally, the third contribution of the thesis consists in an extension of the framework beyond the least-square loss. The stochastic gradient descent algorithm is analyzed as a Markov chain. This point of view leads to an intuitive and insightful interpretation, that outlines the differences between the quadratic setting and the more general setting. A simple method resulting in provable improvements in the convergence is then proposed

9

Boralugoda, Sanath Kumara. "Prox-regular functions in Hilbert spaces." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0006/NQ34740.pdf.

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10

Lapinski, Felicia. "Hilbert spaces and the Spectral theorem." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454412.

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Книги з теми "Hilbert spaces":

1

Janson, Svante. Gaussian Hilbert spaces. Cambridge, U.K: Cambridge University Press, 1997.

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2

Debnath, Lokenath. Hilbert spaces with applications. 3rd ed. Oxford: Academic, 2005.

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3

Mlak, W. Hilbert spaces and operator theory. Dordrecht: Boston, 1991.

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4

Mashreghi, Javad. Hilbert spaces of analytic functions. Providence, R.I: American Mathematical Society, 2010.

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5

Mashreghi, Javad. Hilbert spaces of analytic functions. Providence, R.I: American Mathematical Society, 2010.

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6

Javad, Mashreghi, Ransford Thomas, and Seip Kristian 1962-, eds. Hilbert spaces of analytic functions. Providence, R.I: American Mathematical Society, 2010.

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7

Ma, Tsoy-Wo. Banach-Hilbert spaces, vector measures, and group representations. River Edge, NJ: World Scientific, 2002.

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8

Sarason, Donald. Sub-Hardy Hilbert spaces in the unit disk. New York: Wiley, 1994.

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9

Simon, Jacques. Banach, Fréchet, Hilbert and Neumann Spaces. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119426516.

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10

Agler, Jim. Pick interpolation and Hilbert function spaces. Providence, R.I: American Mathematical Society, 2002.

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Частини книг з теми "Hilbert spaces":

1

D’Angelo, John P. "Hilbert Spaces." In Hermitian Analysis, 45–94. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8526-1_2.

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2

Roman, Steven. "Hilbert Spaces." In Advanced Linear Algebra, 263–90. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-2178-2_14.

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3

Ovchinnikov, Sergei. "Hilbert Spaces." In Universitext, 149–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91512-8_7.

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4

Cicogna, Giampaolo. "Hilbert Spaces." In Undergraduate Lecture Notes in Physics, 1–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76165-7_1.

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5

Gasquet, Claude, and Patrick Witomski. "Hilbert Spaces." In Texts in Applied Mathematics, 141–52. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1598-1_16.

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6

Komornik, Vilmos. "Hilbert Spaces." In Lectures on Functional Analysis and the Lebesgue Integral, 3–54. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6811-9_1.

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7

Shima, Hiroyuki, and Tsuneyoshi Nakayama. "Hilbert Spaces." In Higher Mathematics for Physics and Engineering, 73–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/b138494_4.

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8

van der Vaart, Aad W., and Jon A. Wellner. "Hilbert Spaces." In Weak Convergence and Empirical Processes, 49–51. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2545-2_8.

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9

Brokate, Martin, and Götz Kersting. "Hilbert Spaces." In Compact Textbooks in Mathematics, 137–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15365-0_12.

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10

Kubrusly, Carlos S. "Hilbert Spaces." In Elements of Operator Theory, 311–440. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4757-3328-0_5.

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Тези доповідей конференцій з теми "Hilbert spaces":

1

RANDRIANANTOANINA, BEATA. "A CHARACTERIZATION OF HILBERT SPACES." In Proceedings of the Sixth Conference. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704450_0021.

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2

Taddei, Valentina, Luisa Malaguti, and Irene Benedetti. "Nonlocal problems in Hilbert spaces." In The 10th AIMS Conference on Dynamical Systems, Differential Equations and Applications (Madrid, Spain). American Institute of Mathematical Sciences, 2015. http://dx.doi.org/10.3934/proc.2015.0103.

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3

Tang, Wai-Shing. "Biorthogonality and multiwavelets in Hilbert spaces." In International Symposium on Optical Science and Technology, edited by Akram Aldroubi, Andrew F. Laine, and Michael A. Unser. SPIE, 2000. http://dx.doi.org/10.1117/12.408620.

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4

Pope, Graeme, and Helmut Bolcskei. "Sparse signal recovery in Hilbert spaces." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283506.

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5

Małkiewicz, Przemysław. "Physical Hilbert spaces in quantum gravity." In Proceedings of the MG14 Meeting on General Relativity. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813226609_0514.

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6

Khimshiashvili, G. "Loop spaces and Riemann-Hilbert problems." In Geometry and Topology of Manifolds. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2007. http://dx.doi.org/10.4064/bc76-0-19.

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7

Deepshikha, Saakshi Garg, Lalit K. Vashisht, and Geetika Verma. "On weaving fusion frames for Hilbert spaces." In 2017 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2017. http://dx.doi.org/10.1109/sampta.2017.8024363.

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8

Gritsutenko, Stanislav, Elina Biberdorf, and Rui Dinis. "On the Sampling Theorem in Hilbert Spaces." In Computer Graphics and Imaging. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.798-012.

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Tuia, Devis, Gustavo Camps-Valls, and Manel Martinez-Ramon. "Explicit recursivity into reproducing kernel Hilbert spaces." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947266.

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SUQUET, CHARLES. "REPRODUCING KERNEL HILBERT SPACES AND RANDOM MEASURES." In Proceedings of the 5th International ISAAC Congress. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789812835635_0013.

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Звіти організацій з теми "Hilbert spaces":

1

Saraivanov, Michael. Quantum Circuit Synthesis using Group Decomposition and Hilbert Spaces. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1108.

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2

Korezlioglu, H., and C. Martias. Stochastic Integration for Operator Valued Processes on Hilbert Spaces and on Nuclear Spaces. Revision. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada168501.

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3

Fukumizu, Kenji, Francis R. Bach, and Michael I. Jordan. Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada446572.

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4

Teolis, Anthony. Discrete Representation of Signals from Infinite Dimensional Hilbert Spaces with Application to Noise Suppression and Compression. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada453215.

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5

Salamon, Dietmar. Realization Theory in Hilbert Space. Fort Belvoir, VA: Defense Technical Information Center, July 1985. http://dx.doi.org/10.21236/ada158172.

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6

Yao, Jen-Chih. A monotone complementarity problem in Hilbert space. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/7043013.

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Yao, Jen-Chih. A generalized complementarity problem in Hilbert space. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/6930669.

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8

Cottle, Richard W., and Jen-Chih Yao. Pseudo-Monotone Complementarity Problems in Hilbert Space. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada226477.

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9

Kallianpur, G., and V. Perez-Abreu. Stochastic Evolution Equations with Values on the Dual of a Countably Hilbert Nuclear Space. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada174876.

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10

Monrad, D., and W. Philipp. Nearby Variables with Nearby Conditional Laws and a Strong Approximation Theorem for Hilbert Space Valued Martingales. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada225992.

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