Sökning: "Övervakat Lärande"

Visar resultat 1 - 5 av 19 uppsatser innehållade orden Övervakat Lärande.

1. 1. Predicting Breakdowns in Transportation Vehicles using Supervised Learning

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

   Författare :Shuo Li; [2023]
   Nyckelord :Breakdown Prediction; Transportation Vehicles; Diagnose; Gradient Boosting Classifier; Ensemble Model; Förutsägelse av haveri; transportfordon; diagnos; Gradient Boosting Classifier; Ensemblemodell;

   Sammanfattning : Vehicle breakdowns can lead to fatal accidents, increase costs and reduce productivity. Therefore, robust and accurate fault diagnosis and prediction systems are critical to ensure the proper operation of vehicles. Many researchers have used machine learning for the prediction of vehicle breakdowns. LÄS MER

3. 2. Prediction of Persistence to Treatment for Patients with Rheumatoid Arthritis using Deep Learning

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

   Författare :Serkan Arda Yilal; [2023]
   Nyckelord :Variational Autoencoders; Rheumatoid Arthritis; Precision Medicine; Treatment Prediction; Deep Learning; Supervised Learning; Rheumatoid Artrit; Precisionsmedicin; Behandlingsförutsägelse; Djupinlärning; Övervakat lärande;

   Sammanfattning : Rheumatoid Arthritis is an inflammatory joint disease that is one of the most common autoimmune diseases in the world. The treatment usually starts with a first-line treatment called Methotrexate, but it is often insufficient. One of the most common second-line treatments is Tumor Necrosis Factor inhibitors (TNFi). LÄS MER

4. 3. A study about Active Semi-Supervised Learning for Generative Models

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Elisio Fernandes de Almeida Quintino; [2023]
   Nyckelord :Semi-Supervised Learning; Active Learning; Generative Models; Mixture Models; Semi-Övervakad Inlärning; Aktiv Inlärning; Generativa Modeller; Mixturmodeller;

   Sammanfattning : In many relevant scenarios, there is an imbalance between abundant unlabeled data and scarce labeled data to train predictive models. Semi-Supervised Learning and Active Learning are two distinct approaches to deal with this issue. LÄS MER

5. 4. Maximizing the performance of point cloud 4D panoptic segmentation using AutoML technique

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

   Författare :Teng Ma; [2022]
   Nyckelord :LiDAR perception; 4D panoptic segmentation; Hyperparameter Optimization; Deep learning; Automated Machine Learning; LiDAR-uppfattning; 4D-panoptisk segmentering; hyperparameteroptimering; djupinlärning; automatiserad maskininlärning;

   Sammanfattning : Environment perception is crucial to autonomous driving. Panoptic segmentation and objects tracking are two challenging tasks, and the combination of both, namely 4D panoptic segmentation draws researchers’ attention recently. LÄS MER

7. 5. An Industrial Application of Semi-supervised techniques for automatic surface inspection of stainless steel. : Are pseudo-labeling and consistency regularization effective in a real industrial context?

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

   Författare :Mattia Zoffoli; [2022]
   Nyckelord :Deep Learning; Computer Vision; Semi-Supervised Learning; Automatic Inspection; Stainless Steel; Djupt lärande; datorseende; Semi-övervakat lärande; Automatisk inspektion; Rostfritt stål;

   Sammanfattning : Recent developments in the field of Semi-Supervised Learning are working to avoid the bottleneck of data labeling. This can be achieved by leveraging unlabeled data to limit the amount of labeled data needed for training deep learning models. LÄS MER