Sökning: "särdragsextrahering"
Visar resultat 1 - 5 av 7 uppsatser innehållade ordet särdragsextrahering.
1. Feature extraction from MEG data using self-supervised learning : Investigating contrastive representation learning methods to f ind informative representations
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Modern day society is vastly complex, with information and data constantly being posted, shared, and collected everywhere. There is often an abundance of massive amounts of unlabeled data that can not be leveraged in a supervised machine learning context. LÄS MER
2. Scar detection using deep neural networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Object detection is a computer vision method that deals with the tasks of localizing and classifying objects within an image. The number of usages for the method is constantly growing, and this thesis investigates the unexplored area of using deep neural networks for scar detection. LÄS MER
3. Feature extraction and cluster analysis of oil slicks using optical satellite data
Master-uppsats, Umeå universitet/Institutionen för matematik och matematisk statistikSammanfattning : .... LÄS MER
4. Monitoring Vehicle Suspension Elements Using Machine Learning Techniques
Master-uppsats, KTH/SpårfordonSammanfattning : Condition monitoring (CM) is widely used in industry, and there is a growing interest in applying CM on rail vehicle systems. Condition based maintenance has the possibility to increase system safety and availability while at the sametime reduce the total maintenance costs. LÄS MER
5. PCA based dimensionality reduction of MRI images for training support vector machine to aid diagnosis of bipolar disorder
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This study aims to investigate how dimensionality reduction of neuroimaging data prior to training support vector machines (SVMs) affects the classification accuracy of bipolar disorder. This study uses principal component analysis (PCA) for dimensionality reduction. LÄS MER