Sökning: "Aggregating"
Visar resultat 1 - 5 av 99 uppsatser innehållade ordet Aggregating.
1. Using NeRF- and Mesh-Based Methods to Improve Visualisation of Point Clouds
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : In recent years, the field of generating synthetic images from novel view points has seen some major improvements. Most importantly with the publication of Neural Radiance Fields allowing for extremely detailed and accurate 3D novel views. LÄS MER
2. Decision Trees for Classification of Repeated Measurements
Kandidat-uppsats, Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakultetenSammanfattning : Classification of data from repeated measurements is useful in various disciplines, for example that of medicine. This thesis explores how classification trees (CART) can be used for classifying repeated measures data. LÄS MER
3. WHO’S AFRAID OF COMPLEXITY? An Exploration of the Influence of Native Language Complexity on L2 Complexity
Master-uppsats, Göteborgs universitet / Institutionen för filosofi, lingvistik och vetenskapsteoriSammanfattning : The matter of linguistic complexity has been widely scrutinised in the last few decades, within theoretical linguistics, as well as in second language acquisition studies. A concept introduced in the last half of the previous century, it continues to be a matter of debate in the linguistic field, as it eludes a clear-cut definition and interpretation. LÄS MER
4. Over-the-Air Federated Learning with Compressed Sensing
Master-uppsats, Linköpings universitet/KommunikationssystemSammanfattning : The rapid progress with machine learning (ML) technology has solved previously unsolved problems, but training these ML models requires ever larger datasets and increasing amounts of computational resources. One potential solution is to enable parallelization of the computations and allow local processing of training data in distributed nodes, such as Federated Learning (FL). LÄS MER
5. Aggregating predictions of a yeast semantic segmentation model : Reducing a pixel classifier into a binary image classifier
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The introduction of machine learning in clinical microbiology is important for aiding clinical laboratories with highly repetitive tasks that are fatiguing, error-prone, and require long employee training time due to the complex nature of the task. A challenging task that belongs to the subareas that need assistance is yeast detection in fluorescence microscopy where various yeast morphologies exist. LÄS MER
