Relevant bibliographies by topics / Strictly positive definite kernels

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Strictly positive definite kernels'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Strictly positive definite kernels"

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Park, Il Memming, Sohan Seth, Murali Rao, and José C. Príncipe. "Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences." Neural Computation 24, no. 8 (2012): 2223–50. http://dx.doi.org/10.1162/neco_a_00309.

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Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing sta
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Menegatto, Valdir A. "Strictly Positive Definite Kernels on the Circle." Rocky Mountain Journal of Mathematics 25, no. 3 (1995): 1149–63. http://dx.doi.org/10.1216/rmjm/1181072211.

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Guella, J., and V. A. Menegatto. "Strictly Positive Definite Kernels on the Torus." Constructive Approximation 46, no. 2 (2016): 271–84. http://dx.doi.org/10.1007/s00365-016-9354-2.

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Menegatto, Valdir A. "Strictly positive definite kernels on the hilbert sphere." Applicable Analysis 55, no. 1-2 (1994): 91–101. http://dx.doi.org/10.1080/00036819408840292.

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Guella, J. C., and V. A. Menegatto. "Unitarily invariant strictly positive definite kernels on spheres." Positivity 22, no. 1 (2017): 91–103. http://dx.doi.org/10.1007/s11117-017-0502-0.

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Guella, J. C., and V. A. Menegatto. "Strictly positive definite kernels on a product of spheres." Journal of Mathematical Analysis and Applications 435, no. 1 (2016): 286–301. http://dx.doi.org/10.1016/j.jmaa.2015.10.026.

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Guella, J. C., V. A. Menegatto, and A. P. Peron. "Strictly positive definite kernels on a product of circles." Positivity 21, no. 1 (2016): 329–42. http://dx.doi.org/10.1007/s11117-016-0425-1.

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Barbosa, V. S., and V. A. Menegatto. "Strictly positive definite kernels on compact two-point homogeneous spaces." Mathematical Inequalities & Applications, no. 2 (2016): 743–56. http://dx.doi.org/10.7153/mia-19-54.

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Menegatto, V. A., C. P. Oliveira, and A. P. Peron. "Strictly positive definite kernels on subsets of the complex plane." Computers & Mathematics with Applications 51, no. 8 (2006): 1233–50. http://dx.doi.org/10.1016/j.camwa.2006.04.006.

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Politis, Dimitris N. "HIGHER-ORDER ACCURATE, POSITIVE SEMIDEFINITE ESTIMATION OF LARGE-SAMPLE COVARIANCE AND SPECTRAL DENSITY MATRICES." Econometric Theory 27, no. 4 (2011): 703–44. http://dx.doi.org/10.1017/s0266466610000484.

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A new class of large-sample covariance and spectral density matrix estimators is proposed based on the notion of flat-top kernels. The new estimators are shown to be higher-order accurate when higher-order accuracy is possible. A discussion on kernel choice is presented as well as a supporting finite-sample simulation. The problem of spectral estimation under a potential lack of finite fourth moments is also addressed. The higher-order accuracy of flat-top kernel estimators typically comes at the sacrifice of the positive semidefinite property. Nevertheless, we show how a flat-top estimator ca
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Dissertations / Theses on the topic "Strictly positive definite kernels"

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Bonfim, Rafaela Neves. "Núcleos isotrópicos e positivos definidos sobre espaços 2-homogêneos." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-22092017-105842/.

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Este trabalho é composto de duas partes distintas, ambas dentro de um mesmo tema: núcleos positivos definidos sobre variedades. Na primeira delas fornecemos uma caracterização para os núcleos contínuos, isotrópicos e positivos definidos a valores matriciais sobre um espaço compacto 2-homogêneo. Utilizando-a, investigamos a positividade definida estrita destes núcleos, apresentando inicialmente algumas condições suficientes para garantir tal propriedade. No caso em que o espaço 2-homogêneo não é uma esfera, descrevemos uma caracterização definitiva para a positividade definida estrita do núcleo
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Berschneider, Georg J. [Verfasser]. "Conditionally Positive Definite Kernels : An Abstract Approach / Georg J Berschneider." Aachen : Shaker, 2010. http://d-nb.info/1106838777/34.

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Alsulaimani, Hamdan. "STRICT REGULARITY OF POSITIVE DEFINITE TERNARY QUADRATIC FORMS." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1294.

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An integral quadratic form is said to be strictly regular if it primitively represents all integers that are primitively represented by its genus. The goal of this dissertation is to extend the systematic investigation of the positive definite ternary primitive integral quadratic forms and lattices that are candidates for strict regularity. An integer that is primitively represented by a genus, but not by some specific form in that genus, is called a primitive exception for that form. So, the strictly regular forms are those forms for which there are no primitive exceptions. Our computations of
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Sabree, Aqeeb A. "Positive definite kernels, harmonic analysis, and boundary spaces: Drury-Arveson theory, and related." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/7023.

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A reproducing kernel Hilbert space (RKHS) is a Hilbert space $\mathscr{H}$ of functions with the property that the values $f(x)$ for $f \in \mathscr{H}$ are reproduced from the inner product in $\mathscr{H}$. Recent applications are found in stochastic processes (Ito Calculus), harmonic analysis, complex analysis, learning theory, and machine learning algorithms. This research began with the study of RKHSs to areas such as learning theory, sampling theory, and harmonic analysis. From the Moore-Aronszajn theorem, we have an explicit correspondence between reproducing kernel Hilbert spaces (RKHS
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Neretin, Yurii A., and neretin@main mccme rssi ru. "Matrix Balls, Radial Analysis of Berezin Kernels, and Hypergeometric." ESI preprints, 2000. ftp://ftp.esi.ac.at/pub/Preprints/esi974.ps.

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Ma, Nicholas. "Stationary time series resulting from certain positive definite kernels and simulation via high-order vector autoregressive models." Thesis, Wichita State University, 2013. http://hdl.handle.net/10057/6826.

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Vector auto-regressive models have traditionally been used to model and forecast multivariate time series data, predicting future values based on previous observations. In this thesis, we introduce some multivariate time series with power-law decaying covariance matrix functions, and then construct a VAR model in order to generate approximate data from that time series. A fast model is developed to solve for the VAR(p) coefficients, implementing a block-Toeplitz equation solver to enable the choice of large p, avoiding the memory and speed issues with solving large systems via Gaussian elimina
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Guella, Jean Carlo. "Os critérios de Polya na esfera." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-27112015-102004/.

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Neste trabalho apresentamos uma demonstração detalhada para um conhecido teorema de I. J. Schoenberg que caracteriza certas funções positivas definidas em esferas. Analisamos ainda um critério para a obtenção de positividade definida de uma função a partir de condições de suavidade e convexidade dela, em uma tentativa de ratificar alguns resultados da literatura conhecidos como critérios de Pólya.<br>In this work we present a proof for a famous theorem of Schoenberg on positive definite functions on spheres. We analyze some results that deduce positive definiteness from diferentiability an
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Ferreira, Jose Claudinei. "Decaimento dos autovalores de operadores integrais gerados por núcleos positivos definidos." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-01042008-091207/.

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Inicialmente, estudamos alguns resultados clássicos da teoria dos núcleos positivos definidos e alguns resultados pertinentes. Estudamos em seguida, o Teorema de Mercer e algumas de suas generalizações e conseqüências, incluindo a caracterização da transformada de Fourier de um núcleo positivo definido com domínio Rm£Rm, m ¸ 1. O trabalho traz um enfoque especial nos núcleos cujo domínio é um subconjunto não-compacto de Rm £ Rm, uma vez que os demais casos são considerados de maneira extensiva na literatura. Aplicamos esses estudos na análise do decaimento dos autovalores de operadores integra
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Barbosa, Victor Simões. "Universalidade e ortogonalidade em espaços de Hilbert de reprodução." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-18032013-142251/.

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Neste trabalho analisamos o papel das funções layout de um núcleo positivo definido K sobre um espaço topológico de Hausdor E com relação a duas propriedades específicas: a universalidade de K e a ortogonalidade no espaço de Hilbert de reprodução de K a partir de suportes disjuntos. As funções layout sempre existem mas podem não ser únicas. De uma maneira geral, a função layout e uma aplicação que transfere, convenientemente, informações do espaço E para um espaço com produto interno de dimensão alta, onde métodos lineares podem ser usados. Tanto a universalidade quanto a ortogonalidade pressu
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Barbosa, Victor Simões. "Núcleos positivos definidos em espaços 2-homogêneos." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-02122016-102032/.

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Neste trabalho analisamos a positividade definida estrita de núcleos contínuos sobre um espaço compacto 2-homogêneo. R. Gangolli (1967) apresentou uma caracterização completa para os núcleos que são contínuos, isotrópicos e positivos definidos sobre um espaço compacto 2-homogêneo Md: a parte isotrópica do núcleo é uma série de Fourier uniformemente convergente, com coeficientes não negativos, em relação a certos polinômios de Jacobi atrelados a Md. Uma das contribuições de nosso trabalho é uma caracterização para a positividade definida estrita de tais núcleos, complementando a caracterização
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Books on the topic "Strictly positive definite kernels"

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Bozejko, M. Positive definite kernels, length functions on groups and noncommutative von Neumann inequality. Mathem. Inst. Univ. of Wroclaw, 1987.

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Book chapters on the topic "Strictly positive definite kernels"

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Cheney, Ward, and Will Light. "Strictly positive definite functions." In Graduate Studies in Mathematics. American Mathematical Society, 2009. http://dx.doi.org/10.1090/gsm/101/13.

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Cortes, Corinna, Patrick Haffner, and Mehryar Mohri. "Positive Definite Rational Kernels." In Learning Theory and Kernel Machines. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_5.

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Constantinescu, Tiberiu. "Factorization of Positive Definite Kernels." In Schur Parameters, Factorization and Dilation Problems. Birkhäuser Basel, 1996. http://dx.doi.org/10.1007/978-3-0348-9108-0_5.

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Schaback, Robert, and Holger Wendland. "Approximation by Positive Definite Kernels." In Advanced Problems in Constructive Approximation. Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-7600-1_15.

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Młotkowski, Wojciech. "Positive and Negative Definite Kernels on Trees." In Harmonic Analysis and Discrete Potential Theory. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-2323-3_10.

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Rachev, Svetlozar T., Lev B. Klebanov, Stoyan V. Stoyanov, and Frank J. Fabozzi. "Positive and Negative Definite Kernels and Their Properties." In The Methods of Distances in the Theory of Probability and Statistics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4869-3_21.

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Davydov, Oleg. "Approximation with Conditionally Positive Definite Kernels on Deficient Sets." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57464-2_3.

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Alpay, Daniel. "Positive Definite Functions and Kernels, and Reproducing Kernel Hilbert Spaces." In An Advanced Complex Analysis Problem Book. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16059-7_7.

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"Reproducing Kernel Hilbert Spaces and Native Spaces for Strictly Positive Definite Functions." In Meshfree Approximation Methods with Matlab. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812708632_0013.

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"Positive Definite Kernels." In Linear Algebra and Optimization with Applications to Machine Learning. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811216572_0017.

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Conference papers on the topic "Strictly positive definite kernels"

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Oneto, Luca, Alessandro Ghio, Sandro Ridella, and Davide Anguita. "Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280413.

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Dolloff, John, Brian Lofy, Alan Sussman, and Charles Taylor. "Strictly positive definite correlation functions." In Defense and Security Symposium, edited by Ivan Kadar. SPIE, 2006. http://dx.doi.org/10.1117/12.663967.

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Szafraniec, Franciszek Hugon. "Murphy's ``Positive definite kernels and Hilbert C*-modules'' reorganized." In Noncommutative Harmonic Analysis with Applications to Probability II. Institute of Mathematics Polish Academy of Sciences, 2010. http://dx.doi.org/10.4064/bc89-0-19.

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Popescu, Claudiu, Lacrimioara Grama, and Corneliu Rusu. "On the use of positive definite symmetric kernels for summary extraction." In 2020 13th International Conference on Communications (COMM). IEEE, 2020. http://dx.doi.org/10.1109/comm48946.2020.9142041.

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Accardi, Luigi, Andreas Boukas, and Vladimir Dobrev. "Random Variables and Positive Definite Kernels Associated with the Schrödinger Algebra." In LIE THEORY AND ITS APPLICATIONS IN PHYSICS: VIII International Workshop. AIP, 2010. http://dx.doi.org/10.1063/1.3460158.

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Kim, Geewook, Akifumi Okuno, Kazuki Fukui, and Hidetoshi Shimodaira. "Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/699.

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We propose weighted inner product similarity (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values. Despite its simplicity, WIPS can approximate arbitrary general similarities including positive definite, conditionally positive definite, and indefinite kernels. WIPS is free from similarity model selection, since it can learn any similarity models such as cosine similarity, negative Poincaré distance and negative Wasserstein distance. Our experiments show that the p
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Xue, Hui, Yu Song, and Hai-Ming Xu. "Multiple Indefinite Kernel Learning for Feature Selection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/448.

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Multiple kernel learning for feature selection (MKL-FS) utilizes kernels to explore complex properties of features and performs better in embedded methods. However, the kernels in MKL-FS are generally limited to be positive definite. In fact, indefinite kernels often emerge in actual applications and can achieve better empirical performance. But due to the non-convexity of indefinite kernels, existing MKL-FS methods are usually inapplicable and the corresponding research is also relatively little. In this paper, we propose a novel multiple indefinite kernel feature selection method (MIK-FS) ba
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Chan, Yu-Chin, Faez Ahmed, Liwei Wang, and Wei Chen. "METASET: An Automated Data Selection Method for Scalable Data-Driven Design of Metamaterials." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22681.

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Abstract Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to accelerate the process. However, the dependence on data induces a unique challenge: An imbalanced dataset containing more of certain shapes or physical properties than others can be detrimental to the efficacy of the approaches and any models
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Balas, Mark J., and Susan A. Frost. "A Stabilization of Fixed Gain Controlled Infinite Dimensional Systems by Augmentation With Direct Adaptive Control." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3726.

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Linear infinite dimensional systems are described by a closed, densely defined linear operator that generates a continuous semigroup of bounded operators on a general Hilbert space of states and are controlled via a finite number of actuators and sensors. Many distributed applications are included in this formulation, such as large flexible aerospace structures, adaptive optics, diffusion reactions, smart electric power grids, and quantum information systems. We have developed the following stability result: an infinite dimensional linear system is Almost Strictly Dissipative (ASD) if and only
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Reports on the topic "Strictly positive definite kernels"

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Ron, Amos, and Xingping Sun. Strictly Positive Definite Functions on Spheres. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada276471.

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