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 34 for “"Kernel functions"”.
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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, …
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An Efficient Ranking and Classification Method for Linear Functions, Kernel Functions, Decision Trees, and Ensemble Methods
Structural algorithms incorporate the interdependence of outputs into the prediction, the loss, or both. Frank-Wolfe optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 …
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Computational Study of Kernel - Based Interior - Point Method for LCP
… this thesis, after introducing and analyzing a kernel-based primal-dual interior-point method (IPM) for solving LCP, we consider several, fairly general, eligible kernel functions. We show that the algorithm, with some of those kernel functions, has comparable complexity with the best complexity …
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Discrete Stochastic Modeling of Ion Channels
Kernel representations are obtained for the concentration and flux away from equilibrium, where the kernel functions have a probabilistic interpretation. This is done in a discrete space model and also in its diffusion limit. In particular, the kernel representation is given for the flux at a …
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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 …
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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 …
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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 …
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Optimal weight settings in locally weighted regression: A guidance through cross-validation approach
… or function. This paper explores the different kernel functions used to assign weights to observations, including Gaussian, bi-square, and tri-cubic, and how the choice of weight variables and window size affects the accuracy of the estimates. We guide this choice through the cross-validation …
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Re-thinking termination guarantee of eBPF
In the rapidly evolving landscape of BPF as kernel extensions, where the industry is deploying an increasing count of simultaneously running BPF programs, the need for accounting BPF- induced overhead on latency-sensitive kernel functions is becoming critical. We also find that eBPF's termination …
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Greedy Feature Selection in Tree Kernel Spaces
Tree Kernel functions are powerful tools for solving different classes of problems requiring large amounts of structured information. Combined with accurate learning algorithms, such as Support Vector Machines, they allow us to directly encode rich syntactic data in our learning problems without …
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Generalized multi-peaked model of electoral preferences
… true/typical points of view of a group, and Kernel functions, i.e., deviations from the modes, into the model. I show that Kernel functions bear all the responsibility for the high rates of consistency among aggregation methods, as well as the rarity of cyclical social preferences in …
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EKFuzz: Fuzzing the BPF subsystem
… the way developers interact with the Linux kernel by enabling safe, dynamic programmability. However, this flexibility comes at a cost. The new kernel functions (kfuncs) exposed to eBPF programs are rapidly proliferating, often without adequate testing. While prior work has addressed …
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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 …
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Vehicle Lane Departure Prediction Based On Support Vector Machines
… of the SVMs by experimentally finding their best kernel functions and parameter values as well as the most appropriate vehicle variables as their input variables. Our experiment results involving the 22 drivers with a total of over 6.84 million prediction decisions demonstrate that: (1) the …
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Least squares mixture decomposition estimation
… technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized weight to each data point via the quadratic programming under the Mean Integrated Squared Error …
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Risk prediction with genomic data
… a Support Vector Machine (SVM) with different Kernel functions. For this study, only SNPs generated using genotyping technology, which focuses more on common variants, are used initially for disease prediction. Later, the whole exome data generated using Next Generation Sequencing (NSG) …
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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 …
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The proxy point method for rank-structured matrices
… compress interaction blocks defined by specific kernel functions over points. We address several critical problems of the proxy point method which limit its applicability. A general form of the method is then proposed, paving the way for its wider application in the construction of different …
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Data-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehicles
… control (MRAC) algorithms which make use of kernel functions for learning functional uncertainty present in the system dynamics. The first method extends recent results on model reference adaptive control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases …
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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>
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