Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 25 for “"Kernel method"”.
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Kernel method in Monte Carlo importance sampling
… Since the efficiency of the importance sampling method depends primarily on the choice of the importance sampling density, the use of the kernel method to estimate the optimal importance sampling density is proposed.
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Superconvergence in Iterated Solutions of Integral Equations
… from the Galerkin as well as the collocation method is higher at the knots than we might expect from the numerical solutions that are obtained by applying a class of piecewise polynomials as approximating functions. The type of superconvergence that we investigate in this thesis is different. …
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Wavelet Collocation Method for Hammerstein Integral Equations of High Dimension
… first part, we extend fast wavelets collocation method and multilevel augmentation method on three dimensional Hammerstein equation with both smooth kernel and weakly singular kernel. In this part, self similar partition on d-dimensional (<em>d</em> ≥ 3) unit cube will be introduced and followed …
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Smooth regression quantile estimation
… will be mainly focused on the local linear kernel regression quantile estimation. Different estimators within this class have been proposed, developed asymptotically and applied to real applications. I include algorithmdesign and selection of smoothing parameters. Chapter 2 studies two …
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Novel methods for biological network inference: an application to circadian Ca2+ signaling network
… main goal of this thesis is to develop advanced methods to build models for biological networks, taking the circadian system of Arabidopsis thaliana as a case study. A variety of network inference approaches have been proposed in the literature to study dynamic biological networks. However, many …
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Calibration of Option Pricing in Reproducing Kernel Hilbert Space
… to be ill-posed. We propose a regularization method and reformulate our calibration problem as a problem of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility function which allows us to embrace both the financial and time factors of the options. We …
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A Bayesian classification framework with label corrections
… on top of any parametric or non-parametric method, such as the kernel method and Dirichlet Process (DP) priors. With a thorough study of the kernel and Dirichlet Process method, we successfully applied our framework onto these non-parametric methods and achieved satisfactory results in …
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Winter movements and habitat use of northern saw-whet owls at Assateague Island, Maryland
… at Assateague Island, Maryland. I used the fixed kernel method to calculate home range estimates for seven owls. Ninety-five percent home range sizes averaged 122.9 ha (range from 39.7--261.9 ha) but varied in size and shape. I also calculated adaptive kernel, harmonic mean, and minimum convex …
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Boundary method-based domain decomposition on multiprocessors
An efficient method has been developed for the fast solution of the boundary problems of Poisson's equation on irregular as well as regular domains. The method, called the boundary method-based domain decomposition or BMDD, combines the attractiveness of the domain decomposition technique in …
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Air pollution modelling over complex topography
… equation has been followed. Two different methods have been explored: puff-models and lagrangian particle models. The eulero-lagrangian puff-model CALPUFF (released by Earth Tech) has been used as a reference: closures and parametrizations adopted by this software have been tested over …
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Practical Aspects Of Kernel Smoothing For Binary Regression And Density Estimation
This thesis explores the practical use of kernel smoothing in three areas: binary regression, density estimation and Poisson regression sample size calculations. Both nonparametric and semiparametric binary regression estimators are examined in detail, and extended to two bandwidth cases. The …
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Discriminant analysis : a review of its application to the classificationof grape cultivars
… such as the K-nearest neighbour technique, the kernel method and ranking are briefly discussed. Chapter 3 deals with estimating the probability of misclassification. In Chapter 4 variable selection techniques are discussed. Chapter 5 briefly deals with sequential and logistical discrimination …
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Multivariate Statistical Analysis In Single Cell Transcriptomics
… galore, ranging from purely taxonomical to methodological and computational. This thesis deals with the latter. Coping with the hyper-diversity of cells requires fundamental conceptual advances in computational biology. Chapter 1 sets the stage for the subsequent sections that follow, and …
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Mechanisms of turbulent flame propagation
… turbulent burning velocities, made by the double kernel method during explosions. Turbulence was created by four high speed fans driven by electric motors within the explosion vessel. This arrangement created a central region of uniform, isotropic turbulence in which all measurements were made. …
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Scalable learning for geostatistics and speaker recognition
With improved data acquisition methods, the amount of data that is being collected has increased severalfold. One of the objectives in data collection is to learn useful underlying patterns. In order to work with data at this scale, the methods not only need to be effective with the underlying …
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Convergence of Kernel Methods for Modeling and Estimation of Dynamical Systems
… also arise regarding the reliability of these methods. Recent developments in approximation theory provide a new perspective to studying these problems. This dissertation analyzes the convergence of two kernel-based, data-driven modeling methods, the reproducing kernel Hilbert space (RKHS) …
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Population Characteristics of White-Tailed Deer During the Agricultural Production Season in Northeastern South Dakota
… home ranges were calculated using the adaptive kernel method in program CALHOME, and habitat use patterns were evaluated with a Geographical Information System. Deer densities on SLNWR (1995, 24. 7 � 1. 4 deer/krn2; 1996, 23.7 + 8. 6 deer/krn2) were significantly higher (P < 0. 05) than on PAL …
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Winter/Spring Population Characteristics of White-Tailed Deer in an Agricultural/Wetland Complex in Northeastern South Dakota
… contours that were estimated using an adaptive kernel method. Agricultural crops (i.e., corn [Zea mays], row crops other than corn [e.g., soybeans [Glycine max]], small grains [e. g., wheat [Triticum aestivum]], and alfalfa [Medicago sativa]) were generally preferred in winter within the 95% …
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Structured support vector machines learning and application in computer vision
… over neighboring pixels. With the bundle method, we effectively reduce the number of constraints and alleviate the adverse effect of outliers, leading to an efficient and robust learning algorithm. Moreover, we derive the dual form of the structured SVR algorithm to fit in non-linear cases …
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Structured support vector machines learning and application in computer vision
… over neighboring pixels. With the bundle method, we effectively reduce the number of constraints and alleviate the adverse effect of outliers, leading to an efficient and robust learning algorithm. Moreover, we derive the dual form of the structured SVR algorithm to fit in non-linear cases …
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