Sökning: "machine learning"
Visar resultat 1 - 5 av 3745 uppsatser innehållade orden machine learning.
1. Optimizing on-chip Machine Learning for Data PrefetchingKandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik
Sammanfattning : The idea behind data prefetching is to speed up program execution by predicting what data is needed by the processor, before it is actually needed. Data prefetching is commonly performed by prefetching the next memory address in line, but there are other, more sophisticated approaches such as machine learning. LÄS MER
2. ASSESSING PUBLIC OPINION ON ALGORITHMIC FAIRNESS Reviewing practical challenges and the role of contextual factorsMaster-uppsats, Institutionen för tillämpad informationsteknologi
Sammanfattning : AI ethicists often claim that where algorithmic decision-making is impacting human lives, it is crucial to strive for transparency and explainability. As one form of achieving these, some authors have argued for socio-technical design of AI systems that involves the user in the design process. LÄS MER
3. Primary Drivers of Sea Level Variability in the North – Baltic Sea Transition Using Machine LearningMaster-uppsats, Göteborgs universitet/Institutionen för geovetenskaper
Sammanfattning : Global mean sea level is rising, however not uniformly. Regional deviations of sea surface height (SSH) are common due to local drivers, including surface winds, ocean density stratifications, vertical land- & crustal movements and more. LÄS MER
4. Implementations and evaluation of machine learning algorithms on a microcontroller unit for myoelectric prosthesis controlMaster-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknik
Sammanfattning : Using a microcontroller unit to implement different machine learning algorithms for myoelectric prosthesis control is currently feasible. Still there are hardware and timing constraints that need to be accounted for. LÄS MER
5. MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels in Stacking Ensemble LearningKandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)
Sammanfattning : Stacking, also known as stacked generalization, is a method of ensemble learning where multiple base models are trained on the same dataset, and their predictions are used as input for one or more metamodels in an extra layer. This technique can lead to improved performance compared to single layer ensembles, but often requires a time-consuming trial-and-error process. LÄS MER