Sökning: "Gaussiska Processer"

Visar resultat 1 - 5 av 19 uppsatser innehållade orden Gaussiska Processer.

  1. 1. Real-Time Continuous Euclidean Distance Fields for Large Indoor Environments

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Erik Warberg; [2023]
    Nyckelord :Room segmentation; Gaussian processes; Euclidean distance fields; mapping; line segment detection; mobile robots; spectral clustering; Rumssegmentering; Gaussiska processer; Euklidiska avståndsfält; kartläggning; detektering av linjesegment; mobila robotar; spektral klustring;

    Sammanfattning : Real-time spatial awareness is essential in areas such as robotics and autonomous navigation. However, as environments expand and become increasingly complex, maintaining both a low computational load and high mapping accuracy remains a significant challenge. LÄS MER

  3. 2. Adapting to Perceived Safety within Human-Drone Interaction : Explored in proximity space

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Rasmus Rudling; [2022]
    Nyckelord :;

    Sammanfattning : During recent years, drones’ presence in society has grown. They have many use cases, such as capturing video footage, delivering packages, protecting farmers’ land against wildlife, and fighting fires. Even though the amount of interactions differ, the drones somehow interact with humans in these use cases. LÄS MER

  4. 3. Galaxies as Clocks and the Universal Expansion

    Master-uppsats, KTH/Fysik

    Författare :Anders Ahlström Kjerrgren; [2021]
    Nyckelord :cosmology; cosmic chronometers; universal expansion; Hubble parameter; differential ages; galaxy age; relative age; cosmic time; chi-square minimization; monte carlo sampling; gaussian processes; kosmologi; kosmisk kronometer; universums expansion; Hubbleparametern; differentiell ålder; galaxålder; relativ ålder; kosmisk tid; chi-2-minimering; monte carlo-sampling; gaussiska processer;

    Sammanfattning : The Hubble parameter H(z) is a measure of the expansion rate of the universe at redshift z. One method to determine it relies on inferring the slope of the redshift with respect to cosmic time, where galaxy ages can be used as a proxy for the latter. This method is used by Simon et al. LÄS MER

  5. 4. Constrained Gaussian Process Regression Applied to the Swaption Cube

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Adrien Deleplace; [2021]
    Nyckelord :Swaption cube; Constrained Gaussian process regression; No arbitrage; Option pricing; Hamiltonian Monte Carlo; Swaption-kuben; Regression för gaussiska processer med bivillkor;

    Sammanfattning : This document is a Master Thesis report in financial mathematics for KTH. This Master thesis is the product of an internship conducted at Nexialog Consulting, in Paris. This document is about the innovative use of Constrained Gaussian process regression in order to build an arbitrage free swaption cube. LÄS MER

  7. 5. Machine Unlearning and hyperparameters optimization in Gaussian Process regression

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Matthis Manthe; [2021]
    Nyckelord :GDPR; Machine Unlearning; Data removal; Gaussian Process Regression; Product-of-Experts.; RGPD; Désapprentissage; Suppression de données; Gaussian Process regression; Product-of-Experts.; DSF; avlärningen; dataraderingen; Gaussian Process regression; Produkt-av-experter.;

    Sammanfattning : The establishment of the General Data Protection Regulation (GDPR) in Europe in 2018, including the "Right to be Forgotten" poses important questions about the necessity of efficient data deletion techniques for trained Machine Learning models to completely enforce this right, since retraining from scratch such models whenever a data point must be deleted seems impractical. We tackle such a problem for Gaussian Process Regression and define in this paper an efficient exact unlearning technique for Gaussian Process Regression which completely include the optimization of the hyperparameters of the kernel function. LÄS MER