Dissertations / Theses on the topic 'Kernel'
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Shanmugam, Bala Priyadarshini. "Investigation of kernels for the reproducing kernel particle method." Birmingham, Ala. : University of Alabama at Birmingham, 2009. https://www.mhsl.uab.edu/dt/2009m/shanmugam.pdf.
Full textWalls, Jacob. "Kernel." Thesis, University of Oregon, 2015. http://hdl.handle.net/1794/19203.
Full textGeorge, Sharath. "Usermode kernel : running the kernel in userspace in VM environments." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2858.
Full textGuo, Lisong. "Boost the Reliability of the Linux Kernel : Debugging kernel oopses." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066378/document.
Full textWhen a failure occurs in the Linux kernel, the kernel emits an error report called “kernel oops”, summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this thesis, we study a collection of kernel oopses over a period of 8 months from a repository that is maintained by Red Hat. We consider the overall features of the data, the degree to which the data reflects other information about Linux, and the interpretation of features that may be relevant to reliability. We find that the data correlates well with other information about Linux, but that it suffers from duplicate and missing information. We furthermore identify some potential pitfalls in studying features such as the sources of common faults and common failing applications. Furthermore, a kernel oops provides valuable first-hand information for a Linux kernel maintainer to conduct postmortem debugging, since it logs the status of the Linux kernel at the time of a crash. However, debugging based on only the information in a kernel oops is difficult. To help developers with debugging, we devised a solution to derive the offending line from a kernel oops, i.e., the line of source code that incurs the crash. For this, we propose a novel algorithm based on approximate sequence matching, as used in bioinformatics, to automatically pinpoint the offending line based on information about nearby machine-code instructions, as found in a kernel oops. Our algorithm achieves 92% accuracy compared to 26% for the traditional approach of using only the oops instruction pointer. We integrated the solution into a tool named OOPSA, which would relieve some burden for the developers with the kernel oops debugging
Guo, Lisong. "Boost the Reliability of the Linux Kernel : Debugging kernel oopses." Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066378.
Full textWhen a failure occurs in the Linux kernel, the kernel emits an error report called “kernel oops”, summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this thesis, we study a collection of kernel oopses over a period of 8 months from a repository that is maintained by Red Hat. We consider the overall features of the data, the degree to which the data reflects other information about Linux, and the interpretation of features that may be relevant to reliability. We find that the data correlates well with other information about Linux, but that it suffers from duplicate and missing information. We furthermore identify some potential pitfalls in studying features such as the sources of common faults and common failing applications. Furthermore, a kernel oops provides valuable first-hand information for a Linux kernel maintainer to conduct postmortem debugging, since it logs the status of the Linux kernel at the time of a crash. However, debugging based on only the information in a kernel oops is difficult. To help developers with debugging, we devised a solution to derive the offending line from a kernel oops, i.e., the line of source code that incurs the crash. For this, we propose a novel algorithm based on approximate sequence matching, as used in bioinformatics, to automatically pinpoint the offending line based on information about nearby machine-code instructions, as found in a kernel oops. Our algorithm achieves 92% accuracy compared to 26% for the traditional approach of using only the oops instruction pointer. We integrated the solution into a tool named OOPSA, which would relieve some burden for the developers with the kernel oops debugging
Mika, Sebastian. "Kernel Fisher discriminats." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=967125413.
Full textSun, Fangzheng. "Kernel Coherence Encoders." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/252.
Full textKarlsson, Viktor, and Erik Rosvall. "Extreme Kernel Machine." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211566.
Full textBhujwalla, Yusuf. "Nonlinear System Identification with Kernels : Applications of Derivatives in Reproducing Kernel Hilbert Spaces." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0315/document.
Full textThis thesis will focus exclusively on the application of kernel-based nonparametric methods to nonlinear identification problems. As for other nonlinear methods, two key questions in kernel-based identification are the questions of how to define a nonlinear model (kernel selection) and how to tune the complexity of the model (regularisation). The following chapter will discuss how these questions are usually dealt with in the literature. The principal contribution of this thesis is the presentation and investigation of two optimisation criteria (one existing in the literature and one novel proposition) for structural approximation and complexity tuning in kernel-based nonlinear system identification. Both methods are based on the idea of incorporating feature-based complexity constraints into the optimisation criterion, by penalising derivatives of functions. Essentially, such methods offer the user flexibility in the definition of a kernel function and the choice of regularisation term, which opens new possibilities with respect to how nonlinear models can be estimated in practice. Both methods bear strong links with other methods from the literature, which will be examined in detail in Chapters 2 and 3 and will form the basis of the subsequent developments of the thesis. Whilst analogy will be made with parallel frameworks, the discussion will be rooted in the framework of Reproducing Kernel Hilbert Spaces (RKHS). Using RKHS methods will allow analysis of the methods presented from both a theoretical and a practical point-of-view. Furthermore, the methods developed will be applied to several identification ‘case studies’, comprising of both simulation and real-data examples, notably: • Structural detection in static nonlinear systems. • Controlling smoothness in LPV models. • Complexity tuning using structural penalties in NARX systems. • Internet traffic modelling using kernel methods
Karim, Khan Shahid. "Abstract Kernel Management Environment." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1806.
Full textThe Kerngen Module in MATLAB can be used to optimize a filter with regards to an ideal filter; while taking into consideration the weighting function and the spatial mask. To be able to remotely do these optimizations from a standard web browser over a TCP/IP network connection would be of interest. This master’s thesis covers the project of doing such a system; along with an attempt to graphically display three-dimensional filters and also save the optimized filter in XML format. It includes defining an appropriate DTD for the representation of the filter. The result is a working system, with a server and client written in the programming language PIKE.
Jin, Bo. "Evolutionary Granular Kernel Machines." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/15.
Full textAndersson, Björn. "Contributions to Kernel Equating." Doctoral thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-234618.
Full textDhanjal, Charanpal. "Sparse Kernel feature extraction." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64875/.
Full textRademeyer, Estian. "Bayesian kernel density estimation." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.
Full textDissertation (MSc)--University of Pretoria, 2017.
The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.
Statistics
MSc
Unrestricted
Pevný, Tomáš. "Kernel methods in steganalysis." Diss., Online access via UMI:, 2008.
Find full textCorrigan, Andrew. "Kernel-based meshless methods." Fairfax, VA : George Mason University, 2009. http://hdl.handle.net/1920/4585.
Full textVita: p. 108. Thesis co-directors: John Wallin, Thomas Wanner. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Science and Informatics. Title from PDF t.p. (viewed Oct. 12, 2009). Includes bibliographical references (p. 102-107). Also issued in print.
Ho, Ka-Lung. "Kernel eigenvoice speaker adaptation /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20HOK.
Full textIncludes bibliographical references (leaves 56-61). Also available in electronic version. Access restricted to campus users.
Reichenbach, Stephen Edward. "Small-kernel image restoration." W&M ScholarWorks, 1989. https://scholarworks.wm.edu/etd/1539623783.
Full textHaasdonk, Bernard [Verfasser]. "Transformation Knowledge in Pattern Analysis with Kernel Methods: Distance and Integration Kernels / Bernard Haasdonk." aachen : shaker, 2006. http://nbn-resolving.de/urn:nbn:de:bsz:25-opus-23769.
Full textAnsary, B. M. Saif. "High Performance Inter-kernel Communication and Networking in a Replicated-kernel Operating System." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/78338.
Full textMaster of Science
Ceau, Alban. "Kernel. Application et potentiels scientifiques de l’interférométrie pleine pupille : Analyse statistique des observables kernel." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4035.
Full textHigh resolution observations of the sky are made using techniques that fall into two wide categories: imaging, and interferometry. Imaging consists in estimating the spatial intensity distribution of a source by forming an image of this source of a photosensitive plate, historically using chemical processes (a photographic plate), but nowadays electronically, with detectors. Imaging is limited by the quality of images, which can be approximated from the size of the image formed by point source on the detector. The smaller this size, the higher the resolution of an image. Interferometry, the second aforementioned technique, consists in exploiting the wave properties of light to form interference fringes rather than an image. These fringes encode information on the spatial structure of the observed object in their position and contrast Even though interferometry and imaging are two different techniques, and specialists of one or the other tend to form distinct communities, the phenomena that lead to the formation either of an image, or of an interference pattern are fundamentally the same. This enables, under some conditions, the use of techniques originally developed to treat interferometry data on images. One of these techniques allows to construct closure phases, observables that are robust to optical defaults from interferometry observations. if these optical defaults are small enough (with optical path differences smaller than the wavelength), it is possible to form observables analog to these closure phases called kernel phases. The regime in which these observables can be extracted was only attained recently, with the launch of the first space telescopes and the rise of extreme adaptive optics, which can correct in real time the defaults caused by atmospheric turbulence. If these defaults are small enough, images are called "diffraction limited": the response of the telescope can be considered dominated by the effects of diffraction, which depend on the geometry of the entrance aperture, optical defaults can be described ads perturbations of diffraction.In this regime, the structure of the perturbation can be used to build observables it does not affect. These observables are however to robust to all errors. An imperfect modeling on the entrance aperture and the approximations necessary to their construction can lead to systematic errors. Noises in the image also propagate to the observables. To be able to analyze a measurement, it is necessary to know the errors that affect it, and to propagate them to the final parameters deduced from these measurements.The use case we chose to evaluate these techniques was images of cold brown dwarfs produced by the James Webb Space Telescope (JWST), to predict the detection performances of companions around them. Currently, observation of these cold, Y type dwarfs has been made difficult by their very weak luminosity and temperature, which make observing them very difficult in the near infrared, the preferred domain of AO corrected ground based observatories. Thanks to its great sensitivity and stability, JWST will be able to observe these objects with the greatest precision achieved yet. This stability makes images produced by this telescope ideal candidates for kernel analysis.The performances of these detection procedures are then predicted for images of cold brown dwarfs produced by JWST. For these images, we show that binary detections are possible a contrast that can reach 1000 at separations corresponding to the diffraction limit, often considered to be the resolution limit of a telescope. These contrast detection limits make kernel interferometry a powerful method for the detection of low flux binaries. These detection limits strongly depend on the available flux, which determines the error level on each pixel, and therefore the noise that affects the kernel phases
Liang, Zhiyu. "Eigen-analysis of kernel operators for nonlinear dimension reduction and discrimination." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1388676476.
Full textGuardati, Emanuele. "Path integrals and heat kernel." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14608/.
Full textTullo, Alessandra. "Apprendimento automatico con metodo kernel." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23200/.
Full textCheung, Pak-Ming. "Kernel-based multiple-instance learning /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20CHEUNGP.
Full textBrinker, Klaus. "Active learning with kernel machines." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974403946.
Full textPrenov, B., and Nikolai Tarkhanov. "Kernel spikes of singular problems." Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2008/2619/.
Full textZhu, Feng. "Integrity-Based Kernel Malware Detection." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1572.
Full textKellner, Jérémie. "Gaussian models and kernel methods." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10177/document.
Full textKernel methods have been extensively used to transform initial datasets by mapping them into a so-called kernel space or RKHS, before applying some statistical procedure onto transformed data. In particular, this kind of approach has been explored in the literature to try and make some prescribed probabilistic model more accurate in the RKHS, for instance Gaussian mixtures for classification or mere Gaussians for outlier detection. Therefore this thesis studies the relevancy of such models in kernel spaces.In a first time, we focus on a family of parameterized kernels - Gaussian RBF kernels - and study theoretically the distribution of an embedded random variable in a corresponding RKHS. We managed to prove that most marginals of such a distribution converge weakly to a so-called ''scale-mixture'' of Gaussians - basically a Gaussian with a random variance - when the parameter of the kernel tends to infinity. This result is used in practice to device a new method for outlier detection.In a second time, we present a one-sample test for normality in an RKHS based on the Maximum Mean Discrepancy. In particular, our test uses a fast parametric bootstrap procedure which circumvents the need for re-estimating Gaussian parameters for each bootstrap replication
Subhan, Fazli. "Multilevel sparse kernel-based interpolation." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/9894.
Full textFriess, Thilo-Thomas. "Perceptrons in kernel feature spaces." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327730.
Full textXiao, Bai. "Heat kernel analysis on graphs." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440819.
Full textSun, Xinyuan. "Kernel Methods for Collaborative Filtering." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/135.
Full textEvgeniou, Theodoros K. (Theodoros Kostantinos) 1974. "Learning with kernel machine architectures." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86442.
Full textIncludes bibliographical references (p. 99-106).
by Theodoros K. Evgeniou.
Ph.D.
Naish-Guzman, Andrew Guillermo Peter. "Sparse and robust kernel methods." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612420.
Full textHaine, Christopher. "Kernel optimization by layout restructuring." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0639/document.
Full textCareful data layout design is crucial for achieving high performance, as nowadays processors waste a considerable amount of time being stalled by memory transactions, and in particular spacial and temporal locality have to be optimized. However, data layout transformations is an area left largely unexplored by state-of-the-art compilers, due to the difficulty to evaluate the possible performance gains of transformations. Moreover, optimizing data layout is time-consuming, error-prone, and layout transformations are too numerous tobe experimented by hand in hope to discover a high performance version. We propose to guide application programmers through data layout restructuring with an extensive feedback, firstly by providing a comprehensive multidimensional description of the initial layout, built via analysis of memory traces collected from the application binary textit {in fine} aiming at pinpointing problematic strides at the instruction level, independently of theinput language. We choose to focus on layout transformations,translatable to C-formalism to aid user understanding, that we apply and assesson case study composed of two representative multithreaded real-lifeapplications, a cardiac wave simulation and lattice QCD simulation, with different inputs and parameters. The performance prediction of different transformations matches (within 5%) with hand-optimized layout code
Lundberg, Johannes. "Safe Kernel Programming with Rust." Thesis, KTH, Programvaruteknik och datorsystem, SCS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233255.
Full textAtt skriva buggfri kod i ett lågnivåspråk som C är väldigt svårt. C-kompilatorer blir bättre och bättre på att upptäcka buggar men är ännu långt ifrån att kunna garantera buggfri kod. För applikationsprogrammering finns det tillgängligt olika högnivåspråk som abstrakterar bort den manuella minneshanteringen och hjälper med trådsäker programmering. Dock fortfarande så är större delar av operativsystemet och dess kärna är endast skriven i C. Hur kan vi göra programmering i kärnan säkrare? Vad är prestandakonsekvenserna av att använda ett säkrare språk? I denna uppsats ska vi försöka svara på dessa frågor genom att använda språket Rust. Ett programmeringsgränssnitt i Rust är implementerat i kärnan och en nätverksdrivrutin är portad till Rust. Källkoden skriven i Rust är analyserad för att bedömma säkerheten samt prestandan är jämförd mellan C och Rust implementationerna. Det är bevisat att vi kan skriva en drivrutin i enbart säker Rust om vi kan lita på några osäkra funktioner i gränssnittet. Mätningar visar lite bättre prestanda i Rust.
Chen, Xiaoyi. "Transfer Learning with Kernel Methods." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0005.
Full textTransfer Learning aims to take advantage of source data to help the learning task of related but different target data. This thesis contributes to homogeneous transductive transfer learning where no labeled target data is available. In this thesis, we relax the constraint on conditional probability of labels required by covariate shift to be more and more general, based on which the alignment of marginal probabilities of source and target observations renders source and target similar. Thus, firstly, a maximum likelihood based approach is proposed. Secondly, SVM is adapted to transfer learning with an extra MMD-like constraint where Maximum Mean Discrepancy (MMD) measures this similarity. Thirdly, KPCA is used to align data in a RKHS on minimizing MMD. We further develop the KPCA based approach so that a linear transformation in the input space is enough for a good and robust alignment in the RKHS. Experimentally, our proposed approaches are very promising
You, Di. "Model Selection in Kernel Methods." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322581224.
Full textSung, Iyue. "Importance sampling kernel density estimation /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486398528559777.
Full textBloehdorn, Stephan. "Kernel Methods for knowledge structures." [S.l. : s.n.], 2008. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000009223.
Full textHsiao, Roger Wend Huu. "Kernel eigenspace-based MLLR adaptation /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20HSIAO.
Full textStrathmann, Heiko. "Kernel methods for Monte Carlo." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10040707/.
Full textCagnin, Francesco <1991>. "LLDBagility: practical macOS kernel debugging." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16240.
Full textBraun, Mikio Ludwig. "Spectral properties of the kernel matrix and their relation to kernel methods in machine learning." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=978607309.
Full textXiao, Quanwu. "Learning with kernel based regularization schemes /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ma-b30082365f.pdf.
Full text"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [73]-81)
Ramon, Gurrea Elies. "Kernel approaches for complex phenotype prediction." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671296.
Full textLa relación entre fenotipo e información genotípica es considerablemente intrincada y compleja. Los métodos de aprendizaje automático (ML) se han utilizado con éxito para la predicción de fenotipos en una gran variedad de problemas dentro de la genética y la genómica. Sin embargo, los datos biológicos suelen estar estructurados y pertenecer a tipos de datos "no estándar", lo que puede representar un desafío para la mayoría de los métodos de ML. Entre ellos, los métodos de kernel permiten un enfoque muy versátil para manejar diferentes tipos de datos y problemas mediante una familia de funciones llamadas de kernel. El objetivo principal de esta tesis doctoral es el desarrollo y evaluación de enfoques de kernel específicos para la predicción fenotípica, centrándose en problemas biológicos con tipos de datos o diseños experimentales estructurados. En la primera parte, usamos secuencias de proteínas mutadas del VIH (proteasa, transcriptasa inversa e integrasa) para predecir la resistencia a antiretrovirales. Proponemos dos funciones de kernel categóricas (Overlap y Jaccard) que tienen en cuenta las particularidades de los datos de VIH, como las mezclas de alelos. Los kernels propuestos se combinan con máquinas de vector soporte (SVM) y se comparan con dos funciones de kernel estándar (Linear y RBF) y dos métodos que no son de kernel: bosques aleatorios (RF) y un tipo de red neuronal, el perceptrón multicapa. También incluimos en los kernels la importancia relativa de cada posición de la proteína con respecto a la resistencia. Tener en cuenta tanto la naturaleza categórica de los datos como la presencia de mezclas obtenemos sistemáticamente mejores predicciones. El efecto de la ponderación es mayor en los inhibidores de la integrasa y la transcriptasa inversa, lo que puede estar relacionado con diferencias en los patrones mutacionales de las tres enzimas virales. En la segunda parte, ampliamos el estudio anterior para considerar que las posiciones de las proteínas pueden no ser independientes. Las secuencias mutadas se representan como grafos, ponderándose las aristas por la distancia euclidiana entre residuos obtenida por cristalografía de rayos X. A continuación, se calcula un kernel para grafos (el exponential random walk kernel) que integra los kernels Overlap y Jaccard. A pesar de las ventajas potenciales de este enfoque, no observamos una mejora en la capacidad predictiva. En la tercera parte, proponemos un kernel framework para unificar los análisis supervisados y no supervisados del microbioma. Para ello, usamos una misma matriz de kernel para predecir fenotipos usando SVM y visualización a través de análisis de componentes principales con kernels (kPCA). Definimos dos kernels para datos composicionales (Aitchison-RBF y compositional linear) y discutimos la transformación de medidas de beta-diversidad en kernels. El kernel lineal composicional también permite la recuperación de importancias de taxones (firmas microbianas) del modelo SVM. Para datos con estructura espacial y temporal usamos Multiple Kernel Learning y kernels para series temporales, respectivamente. Ilustramos el kernel framework con tres conjuntos de datos: datos de suelos, datos humanos con un componente espacial y, un conjunto de datos longitudinales inéditos sobre producción porcina. Todos los análisis incluyen una comparación con los informes originales (en los dos primeros casos), así como un contraste con los resultados de RF. El kernel framework no solo permite una visión holística de los datos, sino que también da buenos resultados en cada área de aprendizaje. En análisis no supervisados, los principales patrones detectados en los estudios originales se conservan en kPCA. En análisis supervisados, la SVM tiene un rendimiento mayor (o equivalente) a los RF, mientras que las firmas microbianas son coherentes con los estudios originales y la literatura previa.
The relationship between phenotype and genotypic information is considerably intricate and complex. Machine Learning (ML) methods have been successfully used for phenotype prediction in a great range of problems within genetics and genomics. However, biological data is usually structured and belongs to & 'nonstandard' data types, which can pose a challenge to most ML methods. Among them, kernel methods bring along a very versatile approach to handle different types of data and problems through a family of functions called kernels. The main goal of this PhD thesis is the development and evaluation of specific kernel approaches for phenotypic prediction, focusing on biological problems with structured data types or study designs. In the first part, we predict drug resistance from HIV-mutated protein sequences (protease, reverse transcriptase and integrase). We propose two categorical kernel functions (Overlap and Jaccard) that take into account HIV data particularities, such as allele mixtures. The proposed kernels are coupled with Support Vector Machines (SVM) and compared against two well-known standard kernel functions (Linear and RBF) and two nonkernel methods: Random Forests (RF) and the Multilayer Perceptron neural network. We also include a relative weight into the aforementioned kernels, representing the importance of each protein residue regarding drug resistance. Taking into account both the categorical nature of data and the presence of mixtures consistently delivers better predictions. The weighting effect is higher in reverse transcriptase and integrase inhibitors, which may be related to the different mutational patterns in the viral enzymes regarding drug resistance. In the second part, we extend the previous study to consider the fact that protein positions are not independent. Mutated sequences are modeled as graphs, with edges weighted by the Euclidean distance between residues, obtained from crystal three-dimensional structures. A kernel for graphs (the exponential random walk kernel) that integrates the previous Overlap and Jaccard kernels is then computed. Despite the potential advantages of this kernel for graphs, an improvement on predictive ability as compared to the kernels of the first study is not observed. In the third part, we propose a kernel framework to unify unsupervised and supervised microbiome analyses. To do so, we use the same kernel matrix to perform phenotype prediction via SVMs and visualization via kernel Principal Components Analysis (kPCA). We define two kernels for compositional data (Aitchison-RBF and compositional linear) and discuss the transformation of beta-diversity measures into kernels. The compositional linear kernel also allows the retrieval of taxa importances (microbial signatures) from the SVM model. Spatial and time-structured datasets are handled with Multiple Kernel Learning and kernels for time series, respectively. We illustrate the kernel framework with three datasets: a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. Analyses across the three case studies include a comparison with the original reports (for the two former datasets), as well as contrast with results from RF. The kernel framework not only allows a holistic view of data but also gives good results in each learning area. In unsupervised analyses, the main patterns detected in the original reports are conserved in kPCA. In supervised analyses SVM has better (or, in some cases, equivalent) performance than RF, while microbial signatures are consistent with the original studies and previous literature.
Meyer, Jochen. "Renormierungsgruppen-Flussgleichungen im Heat-Kernel-Formalismus." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=96196006X.
Full textKile, Håkon. "Bandwidth Selection in Kernel Density Estimation." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10015.
Full textIn kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing parameter). There are two conceptually different approaches to this problem: a subjective and an objective approach. In this report, we only consider the objective approach, which is based upon minimizing an error, defined by an error criterion. The most common objective bandwidth selection method is to minimize some squared error expression, but this method is not without its critics. This approach is said to not perform satisfactory in the tail(s) of the density, and to put too much weight on observations close to the mode(s) of the density. An approach which minimizes an absolute error expression, is thought to be without these drawbacks. We will provide a new explicit formula for the mean integrated absolute error. The optimal mean integrated absolute error bandwidth will be compared to the optimal mean integrated squared error bandwidth. We will argue that these two bandwidths are essentially equal. In addition, we study data-driven bandwidth selection, and we will propose a new data-driven bandwidth selector. Our new bandwidth selector has promising behavior with respect to the visual error criterion, especially in the cases of limited sample sizes.
Topaloglu, Mehmet Ersan. "Improving Kernel Performance For Network Sniffing." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1097856/index.pdf.
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G Sniffing is computer-network equivalent of telephone tapping. A Sniffer is simply any software tool used for sniffing. Needs of modern networks today are much more than a sniffer can meet, because of high network traffic and load. Some efforts are shown to overcome this problem. Although successful approaches exist, problem is not completely solved. Efforts mainly includes producing faster hardware, modifying NICs (Network Interface Card), modifying kernel, or some combinations of them. Most efforts are either costly or no know-how exists. In this thesis, problem is attacked via modifying kernel and NIC with aim of transferring the data captured from the network to the application as fast as possible. Snort [1], running on Linux, is used as a case study for performance comparison with the original system. A significant amount of decrease in packet lost ratios is observed at resultant system.