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Relevant bibliographies by topics / Exponential Kernel Function

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Academic literature on the topic 'Exponential Kernel Function'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Exponential Kernel Function"

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Wu, Xia, JinRong Wang, and and Jialu Zhang. "Hermite–Hadamard-Type Inequalities for Convex Functions via the Fractional Integrals with Exponential Kernel." Mathematics 7, no. 9 (2019): 845. http://dx.doi.org/10.3390/math7090845.

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In this paper, we establish three fundamental integral identities by the first- and second-order derivatives for a given function via the fractional integrals with exponential kernel. With the help of these new fractional integral identities, we introduce a few interesting Hermite–Hadamard-type inequalities involving left-sided and right-sided fractional integrals with exponential kernels for convex functions. Finally, some applications to special means of real number are presented.

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Zhang, Huaiqing, Yu Chen, and Xin Nie. "Fast Hankel Transforms Algorithm Based on Kernel Function Interpolation with Exponential Functions." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/105469.

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The Pravin method for Hankel transforms is based on the decomposition of kernel function with exponential function. The defect of such method is the difficulty in its parameters determination and lack of adaptability to kernel function especially using monotonically decreasing functions to approximate the convex ones. This thesis proposed an improved scheme by adding new base function in interpolation procedure. The improved method maintains the merit of Pravin method which can convert the Hankel integral to algebraic calculation. The simulation results reveal that the improved method has high

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Pan, Canlin, Yigang Xu, Guoping Ling, Ming Zhang, Ya‐ming Bo, and Chonghu Cheng. "Exponential degenerate kernel function for electromagnetic scattering problems." Electronics Letters 51, no. 25 (2015): 2102–4. http://dx.doi.org/10.1049/el.2015.1401.

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Qian, Hui, and Lun Yu. "Exponential Sampling: A Gibbs Phenomena Removal Model for Finite Rate of Innovation Sampling Framework." Applied Mechanics and Materials 130-134 (October 2011): 470–74. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.470.

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We propose an exponential approximating function as a sampling kernel with finite rate of innovation. The performance of reconstruct non-bandlimited signals from its low frequency components would inevitably induce Gibbs phenomenon. This paper establishes the theoretical model on relationship between sampling kernel filter and parametric reconstruction method of non-bandlimited signals, and designs a new window function exponential sampling kernel filter to removal Gibbs Influence. Simulation results show that, compared to Sinc sampling kernel filter, the reconstruction ability of exponential

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Nisar, K. S., S. R. Mondal, and P. Agarwal. "Composition Formulas of Bessel-Struve Kernel Function." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9560346.

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The object of this paper is to study and develop the generalized fractional calculus operators involving Appell’s functionF3(·)due to Marichev-Saigo-Maeda. Here, we establish the generalized fractional calculus formulas involving Bessel-Struve kernel functionSαλz, λ,z∈Cto obtain the results in terms of generalized Wright functions. The representations of Bessel-Struve kernel function in terms of exponential function and its relation with Bessel and Struve function are also discussed. The pathway integral representations of Bessel-Struve kernel function are also given in this study.

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Khardon, R., D. Roth, and R. A. Servedio. "Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms." Journal of Artificial Intelligence Research 24 (September 1, 2005): 341–56. http://dx.doi.org/10.1613/jair.1655.

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The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One method of increasing the expressiveness of learned hypotheses in this context is to expand the feature set to include conjunctions of basic features. This can be done explicitly or where possible by using a kernel function. Focusing on the well known Perceptron and Winnow algorithms, the paper demonstrates a tradeoff between the computational efficiency with which the algorithm can be run over the expanded feature spa

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FERNANDEZ, RAHUL N., and V. S. VARADARAJAN. "MATRIX AIRY FUNCTIONS FOR COMPACT LIE GROUPS." International Journal of Mathematics 20, no. 08 (2009): 945–77. http://dx.doi.org/10.1142/s0129167x09005595.

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The classical Airy function has been generalized by Kontsevich to a function of a matrix argument, which is an integral over the space of skew-hermitian matrices of a unitary-invariant exponential kernel. In this paper, the Kontsevich integral is further generalized to integrals over the Lie algebra of an arbitrary connected compact Lie group, using exponential kernels invariant under the group. The (real) polynomial defining this kernel is said to have the Airy property if the integral defines a function of moderate growth. A very general sufficient criterion for a polynomial to have the Airy

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Fang, Jian, and Grégory Faye. "Monotone traveling waves for delayed neural field equations." Mathematical Models and Methods in Applied Sciences 26, no. 10 (2016): 1919–54. http://dx.doi.org/10.1142/s0218202516500482.

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We study the existence of traveling wave solutions and spreading properties for single-layer delayed neural field equations. We focus on the case where the kinetic dynamics are of monostable type and characterize the invasion speeds as a function of the asymptotic decay of the connectivity kernel. More precisely, we show that for exponentially bounded kernels the minimal speed of traveling waves exists and coincides with the spreading speed, which further can be explicitly characterized under a KPP type condition. We also investigate the case of algebraically decaying kernels where we prove th

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Nurpadilah, Winda, I. Made Sumertajaya, and Muhamad Nur Aidi. "Geographically Weighted Regression with Kernel Weighted Function on Poverty Cases in West Java Province." Indonesian Journal of Statistics and Its Applications 5, no. 1 (2021): 173–81. http://dx.doi.org/10.29244/ijsa.v5i1p173-181.

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Spatial regression analysis is a form of regression model that considers spatial effects. Geographically weighted regression (GWR) is the spatial regression methods that can be used to deal with the problem of spatial diversity. This method generates local model parameter estimates for each observation location. The application of spatial statistics can be done in all areas such as the problem of poverty. Poverty can be influenced by factors of proximity between regions, so that in determining the poverty factor, the proximity factor of the region cannot be ignored. West Java Province is a pro

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Salzo, Saverio, and Johan A. K. Suykens. "Generalized support vector regression: Duality and tensor-kernel representation." Analysis and Applications 18, no. 01 (2019): 149–83. http://dx.doi.org/10.1142/s0219530519410069.

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In this paper, we study the variational problem associated to support vector regression in Banach function spaces. Using the Fenchel–Rockafellar duality theory, we give an explicit formulation of the dual problem as well as of the related optimality conditions. Moreover, we provide a new computational framework for solving the problem which relies on a tensor-kernel representation. This analysis overcomes the typical difficulties connected to learning in Banach spaces. We finally present a large class of tensor-kernels to which our theory fully applies: power series tensor kernels. This type o

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Dissertations / Theses on the topic "Exponential Kernel Function"

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McWhorter, Samuel P. "Fundamental Issues in Support Vector Machines." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500155/.

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This dissertation considers certain issues in support vector machines (SVMs), including a description of their construction, aspects of certain exponential kernels used in some SVMs, and a presentation of an algorithm that computes the necessary elements of their operation with proof of convergence. In its first section, this dissertation provides a reasonably complete description of SVMs and their theoretical basis, along with a few motivating examples and counterexamples. This section may be used as an accessible, stand-alone introduction to the subject of SVMs for the advanced undergraduate

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Saribekir, Gozde. "The Turkish Catastrophe Insurance Pool Claims Modeling 2000-2008 Data." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615755/index.pdf.

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After the 1999 Marmara Earthquake, social, economic and engineering studies on earthquakes became more intensive. The Turkish Catastrophe Insurance Pool (TCIP) was established after the Marmara Earthquake to share the deficit in the budget of the Government. The TCIP has become a data source for researchers, consisting of variables such as number of claims, claim amount and magnitude. In this thesis, the TCIP earthquake claims, collected between 2000 and 2008, are studied. The number of claims and claim payments (aggregate claim amount) are modeled by using Generalized Linear Models (GLM). Obs

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Muševič, Sašo. "Non-stationary sinusoidal analysis." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123809.

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Muchos tipos de señales que encontramos a diario pertenecen a la categoría de sinusoides no estacionarias. Una gran parte de esas señales son sonidos que presentan una gran variedad de características: acústicos/electrónicos, sonidos instrumentales harmónicos/impulsivos, habla/canto, y la mezcla de todos ellos que podemos encontrar en la música. Durante décadas la comunidad científica ha estudiado y analizado ese tipo de señales. El motivo principal es la gran utilidad de los avances científicos en una gran variedad de áreas, desde aplicaciones médicas, financiera y ópticas, a procesado de rad

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Books on the topic "Exponential Kernel Function"

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Delsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Редактори Frédéric Ferraty та Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.

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This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain

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Book chapters on the topic "Exponential Kernel Function"

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Shoji, Isao. "Nonparametric Estimation of Nonlinear Dynamics by Local Linear Approximation." In Chaos and Complexity Theory for Management. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2509-9.ch019.

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This chapter discusses nonparametric estimation of nonlinear dynamical system models by a method of metric-based local linear approximation. By specifying a metric such as the standard metric or the square metric on the Euclidean space and a weighting function based on such as the exponential function or the cut-off function, it is possible to estimate values of an unknown vector field from experimental data. It can be shown the local linear fitting with the Gaussian kernel, or the local polynomial modeling of degree one, is included in the class of the proposed method. In addition, conducting simulation studies for estimating random oscillations, the chapter shows the method numerically works well.

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Conference papers on the topic "Exponential Kernel Function"

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Yang, Dingding, Liming Tang, Shiqiang Chen, and Jun Li. "Image Segmentation Based on Exponential Kernel Function." In 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks (ISPAN), 2017 11th International Conference on Frontiers of Computer Science and Technology (FCST), & 2017 Third International Symposium of Creative Computing (ISCC). IEEE, 2017. http://dx.doi.org/10.1109/ispan-fcst-iscc.2017.31.

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Henninger, Stefan, Peter Jeschke, Graham Ashcroft, and Edmund Kügeler. "Time-Domain Implementation of Higher-Order Non-Reflecting Boundary Conditions for Turbomachinery Applications." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42362.

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The present paper demonstrates the time-domain implementation of arbitrary-order non-reflecting boundary conditions for a 3d non-linear time-accurate RANS solver for turbomachinery applications. The conditions are based on the 2d circumferential mode decomposition of the linearized Euler equations. The exact linearized conditions are non-local since they involve space-time Fourier/Laplace transforms. Time-local conditions of arbitrary order are obtained by approximation of the inverse Laplace transform Bessel function convolution kernel by sums of exponential functions. Likewise, this correspo

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Zhang, Xiang-Song, Wei-Xin Gao, and Shi-Ling Zhu. "Research on Noise Reduction and Enhancement of Weld Image." In 9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101902.

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In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduc

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Dereziński, Michał, Rajiv Khanna, and Michael W. Mahoney. "Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/647.

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The Column Subset Selection Problem (CSSP) and the Nystrom method are among the leading tools for constructing interpretable low-rank approximations of large datasets by selecting a small but representative set of features or instances. A fundamental question in this area is: what is the cost of this interpretability, i.e., how well can a data subset of size k compete with the best rank k approximation? We develop techniques which exploit spectral properties of the data matrix to obtain improved approximation guarantees which go beyond the standard worst-case analysis. Our approach leads to si

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Ryan, Kevin M., Jesper Kristensen, You Ling, Sayan Ghosh, Isaac Asher, and Liping Wang. "A Gaussian Process Modeling Approach for Fast Robust Design With Uncertain Inputs." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-77007.

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Many engineering design and industrial manufacturing applications are tasked with finding optimum designs while dealing with uncertainty in the design parameters. The performance or quality of the design may be sensitive to the input variation, making it difficult to optimize. Probabilistic and robust design optimization methods are used in these scenarios to find the designs that will perform best under the presence of known input uncertainty. Robust design optimization algorithms often require a two-level optimization problem (double-loop) to find a solution. The design optimization outer-lo

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Ippili, Rajani K., Richard D. Widdle, Patricia Davies, and Anil K. Bajaj. "Modeling and Identification of Polyurethane Foam in Uniaxial Compression: Combined Elastic and Viscoelastic Response." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48485.

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Polyurethane foam used in automotive seating applications is a highly nonlinear and viscoelastic material. These properties are manifested even in its quasi-static response. In this paper, two different approaches to model and identify these material properties are presented. In both the approaches the viscoelastic property is assumed linear and modeled by a convolution of the input with a relaxation kernel that is a sum of exponentials (hereditary integral approach). The elastic force contribution is however assumed nonlinear and modeled by a polynomial in one approach, and by a model derived

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Singh, R., P. Davies, and A. K. Bajaj. "Nonlinear Modeling and Identification of the Dynamic Behavior of Polyurethane Foam." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21592.

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Abstract The analysis of the steady-state response of a polyurethane foam and mass system to harmonic excitation is given. The foam’s uni-directional dynamic behavior motion is modeled by using nonlinear stiffness, linear viscoelastic and velocity proportional damping components. The relaxation kernel for the viscoelastic model is assumed to be a sum of exponentials. Harmonic balance is used to develop one- and two-term solution approximations that are utilized for system identification. The identification process is based on least-squares minimization of a sub-optimal cost function that uses

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Praharaj, R. K., and N. Datta. "Application of Fractional Calculus in Modelling Viscoelastic Foundation of Ship Structures for Passive Vibration Control." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18674.

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Abstract Viscoelastic materials (VEM) are often used in the ship floors to dampen the noise and vibration induced by machinery (engines, pumps, alternators) and live loads (human traffic and liquid movement in pipelines). Therefore, accurate modeling of such materials is important for the iterative design-analysis process. VEM exhibits both time and frequency-dependent properties. Traditionally, integer-order viscoelastic mechanical models are used to describe the rheological properties of the viscoelastic materials. However, such models find difficulties in predicting the rheological characte

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Reports on the topic "Exponential Kernel Function"

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Zhao, L. C. Exponential Bounds of Mean Error for the Kernal Estimates of Regression Functions. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada167345.

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