Sökning: "gaussiska processer"

Visar resultat 1 - 5 av 13 uppsatser innehållade orden gaussiska processer.

  1. 1. Early-Stage Prediction of Lithium-Ion Battery Cycle Life Using Gaussian Process Regression

    Master-uppsats, KTH/Matematisk statistik

    Författare :Love Wikland; [2020]
    Nyckelord :Statistical learning; prediction; regression; Gaussian processes; lithium-ion battery; battery health; battery lifetime; Statistisk inlärning; prediction; regression; Gaussiska processer; litiumjonbatteri; batterihälsa; batterilivstid;

    Sammanfattning : Data-driven prediction of battery health has gained increased attention over the past couple of years, in both academia and industry. Accurate early-stage predictions of battery performance would create new opportunities regarding production and use. LÄS MER

  2. 2. Image Distance Learning for Probabilistic Dose–Volume Histogram and Spatial Dose Prediction in Radiation Therapy Treatment Planning

    Master-uppsats, KTH/Matematisk statistik

    Författare :Ivar Eriksson; [2020]
    Nyckelord :Radiation therapy; automated planning; machine learning; autoencoder; distance optimisation; sparse pseudo-input Gaussian process; kernel density estimation; dose mimicking; dose–volume histogram; Strålbehandling; automatiserad dosplanering; maskininlärning; autoencoder; distansoptimering; glesa Gaussiska processer; sannolikhets-fördelnings-estimering; dosrekonstruktion; dos–volymhistogram;

    Sammanfattning : Construction of radiotherapy treatments for cancer is a laborious and time consuming task. At the same time, when presented with a treatment plan, an oncologist can quickly judge whether or not it is suitable. This means that the problem of constructing these treatment plans is well suited for automation. LÄS MER

  3. 3. Automatic Generation of Patient-specific Gamma Knife Treatment Plans for Vestibular Schwannoma Patients

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

    Författare :Simon Löw; [2020]
    Nyckelord :;

    Sammanfattning : In this thesis a new fully automatic process for radiotherapy treatment planning with the Leksell Gamma Knife is implemented and evaluated: First, a machine learning algorithm is trained to predict the desired dose distribution, then a convex optimization problem is solved to find the optimal Gamma Knife configuration using the prediction as the optimization objective. The method is evaluated using Bayesian linear regression, Gaussian processes and convolutional neural networks for the prediction. LÄS MER

  4. 4. Automatic Generation of Patient-specific Gamma Knife Treatment Plans for Vestibular Schwannoma Patients

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

    Författare :Simon Löw; [2020]
    Nyckelord :;

    Sammanfattning : In this thesis a new fully automatic process for radiotherapy treatment planning with the Leksell Gamma Knife is implemented and evaluated: First, a machine learning algorithm is trained to predict the desired dose distribution, then a convex optimization problem is solved to find the optimal Gamma Knife configuration using the prediction as the optimization objective. The method is evaluated using Bayesian linear regression, Gaussian processes and convolutional neural networks for the prediction. LÄS MER

  5. 5. Scalable Gaussian Process Regression for Time Series Modelling

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

    Författare :Vidhyarthi Boopathi; [2019]
    Nyckelord :Distributed Machine learning; Spark; Gaussian Processes; Regression; Time series; Distribuerad maskininlärning; Spark; Gaussiska processer; Regression; Sensormodellering; Tidsserier;

    Sammanfattning : Machine learning algorithms has its applications in almost all areas of our daily lives. This is mainly due to its ability to learn complex patterns and insights from massive datasets. With the increase in the data at a high rate, it is becoming necessary that the algorithms are resource-efficient and scalable. LÄS MER