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.
Results
Showing 1 to 20 of 38 for “"Kernel function"”.
-
Kernel function in direct-current resistivity sounding, The
Includes bibliographical references.
-
Hardy Space Properties of the Cauchy Kernel Function for a Strictly Convex Planar Domain
… $C^2$ in $\mathbb{C}^N$, the Henkin-Ram\'irez Kernel Function belongs to the Smirnov class, $E^q(D)$, for every $q\in(0,N)$.</p> <p>The main objective of this dissertation is to show an analogous result for the Cauchy Kernel Function and for any strictly convex bounded domain in the complex …
-
Interior Point Methods and Kernel Functions of a Linear Programming Problem
… problem (LP) that is based on the generic kernel function is considered. The complexity (in terms of iteration bounds) of the algorithm is first analyzed for a class of kernel functions defined by (3-1). This class is fairly general; it includes classical logarithmic kernel function, …
-
A new computation of the Bergman kernel and related techniques
… introduce a technique for obtaining the Bergman kernel on certain Hartogs domains. To do so, we apply a differential operator to a known kernel function on a domain in lower dimensional space. We rediscover some known results and we obtain new explicit formulas. Using these formulas, we analyze …
-
Kernel Methods for Tree Structured Data
… techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of …
-
Kernel-Based Interior-Point Algorithms for the Linear Complementarity Problem
… and (non-linear) complementary equation. A kernel-based primal-dual Interior-Point Method (IPM) for solving LCP was introduced and analyzed. The class of kernel functions used in this thesis is a class of so-called eligible kernel functions that are fairly general. We have shown for a …
-
Evaluation of different Support Vector Machines (SVM) for speaker identification
… into four support vector machines (SVM) kernels. SVMs have gained much acceptance in classification tasks since their inception in the 1990s. The central feature of SVM is the implicit mapping of input data to some higher-dimensional feature space. This is achieved through the use of …
-
Classifying drug-induced epileptic seizures in zebrafish larvae
… vector machine using second-degree polynomial kernel function. Results indicate 91.33% classification accuracy on training data and when run on the new testing datasets similar classification accuracy was obtained, 90.96%. A future application of this model is to aid in the development of new …
-
Analytic Computation of the Prime -Counting Function
… areas. In this dissertation we: (1) introduce a kernel function which appears to be more effective than the one suggested by Lagarias and Odlyzko; (2) perform a careful analysis of various sources of truncation error; (3) give choices of parameters which bound the truncation error while keeping …
-
Computer aided analysis of skin lesions
… act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer.
-
G-hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases
… search in large graph databases with graph kernel functions. In particular, we propose to develop (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. In our method we use a hash table to support efficient storage and fast search …
-
Relation among images: Modelling, optimization and applications
… relation among them, such as pairwise distance, kernel function and factor analysis. However, existing methods are still insufficient as they suffer from many real factors such as misalignment of images and inefficiency from nonlinearity. This dissertation focuses on improving the relation …
-
Support vector machines, N-gram kernels, and text classification
… methods. One the key components of SVMs are kernel functions. The choice of kernel function can have substantial effects on the performance of SVMs. In this paper we explore kernels based off of N-grams or consecutive sequences of words.</p>
-
Model Reduction of Linear Time-Periodic Dynamical Systems
… a convolution operator of a bivariate periodic kernel function. The kernel suggests a representation of the system as a frequency operator, called the Harmonic Transfer Function. Second, we exploit the Hilbert space structure of the family of LTP systems to develop necessary conditions for …
-
Deep Embedding Kernel
Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having their own unique characteristics, both branches work through mapping data to a feature space that is supposedly more …
-
Multivariate time series clustering using kernel variant multi-way principal component analysis
… time series with high order interactions. Kernel variant MPCA will be proposed for an alternative solution for this case. To test if KMPCA can cluster trivariate time series data into two groups, two simulation studies were conducted. The first study has the same mean structure groups with …
-
One and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles
… resulting data with a nonparametric method like kernel, local polynomial or spline smoothing. We demonstrate both in application and simulation that this two-step procedure of quantile estimation is superior to the parametric quantile regression. We then develop a one-step method which combines …
-
Optimisation of Gaussian process regressions of molecular potential energy surfaces
… problems, the choice of an appropriate kernel function to construct the desired model is not straightforward. The feature spaces, onto which one projects molecular geometries, have many degrees of freedom and do not necessarily consider the physics of the problem. One then has to …
-
Transformation knowledge in pattern analysis with kernel methods - distance and integration kernels
… data analysis and machine learning are so called kernel methods. The most famous and successful one is represented by the support vector machine (SVM) for classification or regression tasks. Further examples are kernel principal component analysis for feature extraction or other linear classifiers …
-
An improved algorithm for iris classification by using support vector machine and binary random machine learning
… using Support Vector Machine with four different kernel function (Linear, Radial Basis, Sigmoid and Polynomial), Random Forest (RF), k-Nearest Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. The first objective of this study is to improve a new algorithm technique for …
Page 1 of 2