Sökning: "oövervakad inlärning"

Hittade 5 avhandlingar innehållade orden oövervakad inlärning.

  1. 1. Visual Analytics for Explainable and Trustworthy Machine Learning

    Författare :Angelos Chatzimparmpas; Andreas Kerren; Rafael M. Martins; Ilir Jusufi; Alex Endert; Linnéuniversitetet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; visualization; interaction; visual analytics; explainable machine learning; XAI; trustworthy machine learning; ensemble learning; dimensionality reduction; supervised learning; unsupervised learning; ML; AI; tabular data; visualisering; interaktion; visuell analys; förklarlig maskininlärning; XAI; pålitlig maskininlärning; ensembleinlärning; dimensionesreducering; övervakad inlärning; oövervakad inlärning; ML; AI; tabelldata; Computer Science; Datavetenskap; Informations- och programvisualisering; Information and software visualization;

    Sammanfattning : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. LÄS MER

  3. 2. Multi-Modal Deep Learning with Sentinel-1 and Sentinel-2 Data for Urban Mapping and Change Detection

    Författare :Sebastian Hafner; Yifang Ban; Paolo Gamba; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Geoinformatics; Geoinformatik;

    Sammanfattning : Driven by the rapid growth in population, urbanization is progressing at an unprecedented rate in many places around the world. Earth observation has become an invaluable tool to monitor urbanization on a global scale by either mapping the extent of cities or detecting newly constructed urban areas within and around cities. LÄS MER

  4. 3. Fast, Robust and Scalable Clustering Algorithms with Applications in Computer Vision

    Författare :Vahan Petrosyan; Alexandre Proutiere; Mikael Johansson; Maki Atsuto; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Datalogi; Computer Science; Datalogi; Computer Science; Electrical Engineering; Elektro- och systemteknik;

    Sammanfattning : In this thesis, we address a number of challenges in cluster analysis. We begin by investigating one of the oldest and most challenging problems: determining the number of clusters, k. LÄS MER

  5. 4. Physics-Informed Neural Networks and Machine Learning Algorithms for Sustainability Advancements in Power Systems Components

    Författare :Federica Bragone; Stefano Markidis; Kateryna Morozovska; Tor Laneryd; Michele Luvisotto; Matthias Ehrhardt; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Physics-Informed Neural Networks; Machine Learning; Data-Driven Methods; Circular Economy; Power Systems Components; Sustainability; Cellulose Nanofibrils; Fysikinformerade Neurala Nätverk; Maskininlärning; Datadrivna Metoder; Cirkulär Ekonomi; Kraftsystemets Komponenter; Hållbarhet; Cellulosananofibriller; Datalogi; Computer Science;

    Sammanfattning : A power system consists of several critical components necessary for providing electricity from the producers to the consumers. Monitoring the lifetime of power system components becomes vital since they are subjected to electrical currents and high temperatures, which affect their ageing. LÄS MER

  7. 5. Supervised and Unsupervised Deep Learning Models for Flood Detection

    Författare :Ritu Yadav; Yifang Ban; Andrea Nascetti; Nicolas Audebert; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Floods; Remote Sensing; Sentinel-1 SAR; Segmentation; Change Detection; DEM; Data Fusion; Time Series; Deep Learning; Unsupervised Learning; Contrastive Learning; Self-Attention; Convolutional LSTM; Variational AutoEncoder VAE ; Geoinformatik; Geoinformatics;

    Sammanfattning : Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. LÄS MER