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Thèses sur le sujet « Kernel filtering »

Auteur : Grafiati

Publié le 6 septembre 2023

Mis à jour le 31 juillet 2025

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1

Sun, Xinyuan. "Kernel Methods for Collaborative Filtering." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/135.

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The goal of the thesis is to extend the kernel methods to matrix factorization(MF) for collaborative ltering(CF). In current literature, MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. The best known member of kernel methods is support vector machine (SVM) on linearly non-separable data. In this thesis, we apply kernel methods on MF, embedding the data into a possibly higher dimensional space and conduct factorization in that space. To improve kernelized matrix factorization, we apply multi-kernel learning methods to selec

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Kabbara, Jad. "Kernel adaptive filtering algorithms with improved tracking ability." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123272.

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In recent years, there has been an increasing interest in kernel methods in areas such as machine learning and signal processing as these methods show strong performance in classification and regression problems. Interesting "kernelized" extensions of many well-known algorithms in artificial intelligence and signal processing have been presented, particularly, kernel versions of the popular online recursive least squares (RLS) adaptive algorithm, namely kernel RLS (KRLS). These algorithms have been receiving significant attention over the past decade in statistical estimation problems, among w

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Bilal, Tahir. "Content Based Packet Filtering In Linux Kernel Using Deterministic Finite Automata." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613710/index.pdf.

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In this thesis, we present a content based packet filtering Architecture in Linux using Deterministic Finite Automata and iptables framework. New generation firewalls and intrusion detection systems not only filter or inspect network packets according to their header fields but also take into account the content of payload. These systems use a set of signatures in the form of regular expressions or plain strings to scan network packets. This scanning phase is a CPU intensive task which may degrade network performance. Currently, the Linux kernel firewall scans network packets separately for ea

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4

Polato, Mirko. "Definition and learning of logic-based kernels for categorical data, and application to collaborative filtering." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3427260.

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The continuous pursuit of better prediction quality has gradually led to the development of increasingly complex machine learning models, e.g., deep neural networks. Despite the great success in many domains, the black-box nature of these models makes them not suitable for applications in which the model understanding is at least as important as the prediction accuracy, such as medical applications. On the other hand, more interpretable models, as decision trees, are in general much less accurate. In this thesis, we try to merge the positive aspects of these two realities, by injecting int

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Fischer, Manfred M., and Peter Stumpner. "Income Distribution Dynamics and Cross-Region Convergence in Europe. Spatial filtering and novel stochastic kernel representations." WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/3969/1/SSRN%2Did981148.pdf.

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This paper suggests an empirical framework for analysing income distribution dynamics and cross-region convergence in the European Union of 27 member states, 1995- 2003. The framework lies in the research tradition that allows the state income space to be continuous, puts emphasis on both shape and intra-distribution dynamics and uses stochastic kernels for studying transition dynamics and implied long-run behaviour. In this paper stochastic kernels are described by conditional density functions, estimated by a product kernel estimator of conditional density and represented by means of n

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Mahfouz, Sandy. "Kernel-based machine learning for tracking and environmental monitoring in wireless sensor networkds." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0025/document.

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Cette thèse porte sur les problèmes de localisation et de surveillance de champ de gaz à l'aide de réseaux de capteurs sans fil. Nous nous intéressons d'abord à la géolocalisation des capteurs et au suivi de cibles. Nous proposons ainsi une approche exploitant la puissance des signaux échangés entre les capteurs et appliquant les méthodes à noyaux avec la technique de fingerprinting. Nous élaborons ensuite une méthode de suivi de cibles, en se basant sur l'approche de localisation proposée. Cette méthode permet d'améliorer la position estimée de la cible en tenant compte de ses accélérations,

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Vaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.

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En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no li

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Suutala, J. (Jaakko). "Learning discriminative models from structured multi-sensor data for human context recognition." Doctoral thesis, Oulun yliopisto, 2012. http://urn.fi/urn:isbn:9789514298493.

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Abstract In this work, statistical machine learning and pattern recognition methods were developed and applied to sensor-based human context recognition. More precisely, we concentrated on an effective discriminative learning framework, where input-output mapping is learned directly from a labeled dataset. Non-parametric discriminative classification and regression models based on kernel methods were applied. They include support vector machines (SVM) and Gaussian processes (GP), which play a central role in modern statistical machine learning. Based on these established models, we propose var

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Verzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.

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With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available. This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences. Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues. In this thesis, we present n

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Hsiao, Ming-Yuen, and 蕭閔元. "Indoor Positioning With Distributed Kernel-Based Bayesian Filtering." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/3328rw.

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碩士<br>國立中興大學<br>電機工程學系所<br>101<br>In the wireless sensor network, several localization algorithms have been proposed for indoor positioning systems. However, the computational complexity of these schemes is high, which may not be suitable to be implemented in sensor nodes. For example, the limited sensor capabilities lead to performing the particle filtering with a very small set of samples, which results in high positioning errors. Hence, a novel sampling scheme may be required to improve estimation accuracy for the particle filter method. In this thesis, the concept of support vector regress

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Ghosh, Sanjay. "Kernel-Based Image Filtering: Fast Algorithms and Applications." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4635.

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Image filtering is a fundamental preprocessing task in computer vision and image processing. Various linear and nonlinear filters are routinely used for enhancement, upsampling, sharpening, reconstruction, etc. The focus of this thesis is on kernel-based filtering that has received significant attention in recent years. The basic idea of kernel filtering is quite straightforward, namely, each pixel p in the image is replaced by a weighted average of its neighboring pixels q. The weighting is performed using a kernel k(p;q), which is nonnegative and symmetric. The weight assigned to a pai

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Tabibiazar, Arash. "Statistical Filtering for Multimodal Mobility Modeling in Cyber Physical Systems." Thesis, 2013. http://hdl.handle.net/10012/7387.

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A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models

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Krishnan, Sunder Ram. "Optimum Savitzky-Golay Filtering for Signal Estimation." Thesis, 2013. http://etd.iisc.ac.in/handle/2005/3293.

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Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the denoising problem as one of estimating the underlying clean signal from noisy measurements by minimizing a risk corresponding to a chosen loss function. Our goal is to incorporate perceptually-motivated loss functions wherever applicable, as in the case of speech enhancement, with the squared error loss being considered for the other scenarios. Since the true risks are observed t

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Krishnan, Sunder Ram. "Optimum Savitzky-Golay Filtering for Signal Estimation." Thesis, 2013. http://hdl.handle.net/2005/3293.

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Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the denoising problem as one of estimating the underlying clean signal from noisy measurements by minimizing a risk corresponding to a chosen loss function. Our goal is to incorporate perceptually-motivated loss functions wherever applicable, as in the case of speech enhancement, with the squared error loss being considered for the other scenarios. Since the true risks are observed t

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Coufal, David. "Jádrové metody v částicových filtrech." Doctoral thesis, 2018. http://www.nusl.cz/ntk/nusl-389580.

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Kernel Methods in Particle Filtering David Coufal Doctoral thesis - abstract The thesis deals with the use of kernel density estimates in particle filtering. In particular, it examines the convergence of the kernel density estimates to the filtering densities. The estimates are constructed on the basis of an out- put from particle filtering. It is proved theoretically that using the standard kernel density estimation methodology is effective in the context of particle filtering, although particle filtering does not produce random samples from the filtering densities. The main theoretical resul

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Viswanath, G. "Robustness And Localization In Time-Varying Spectral Estimation." Thesis, 1997. https://etd.iisc.ac.in/handle/2005/1814.

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Viswanath, G. "Robustness And Localization In Time-Varying Spectral Estimation." Thesis, 1997. http://etd.iisc.ernet.in/handle/2005/1814.

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