Sökning: "method validation"
Visar resultat 1 - 5 av 907 uppsatser innehållade orden method validation.
1. Application of Fourier Transforms to time-resolved ambient pressure spectroscopy operando studies of CO oxidation over Pt(111)
Master-uppsats, Lunds universitet/Fysiska institutionen; Lunds universitet/SynkrotronljusfysikSammanfattning : The study of heterogeneous catalysis has important implications in increasing the efficiency of industrial processes and in reducing the emissions of greenhouse gases from sources such as automobiles. A reaction with high industrial relevance is the oxidation of CO on noble metal catalysts. LÄS MER
2. En undersökning av metoder förautomatiserad text ochparameterextraktion frånPDF-dokument med NaturalLanguage Processing
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : I dagens affärsmiljö strävar många organisationer efter att automatisera processen för att hämta information från fakturor. Målet är att göra hanteringen av stora mängder fakturor mer effektiv. Trots detta möter man utmaningar på grund av den varierande strukturen hos fakturor. LÄS MER
3. Image Quality Assessment Pipeline and Semi-Automated Annotation method for Synthetic Data
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : Predicting human emotions through facial expression, particularly in relation to medication field such as clinical trial settings, has garnered scientific interest in recent years due to significant understanding of the impact of treatment on emotions and social functioning. This thesis aims to improve performance of a FER model using large scale of synthetic data. LÄS MER
4. Point process learning for non-parametric intensity estimation with focus on Voronoi estimation
Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaperSammanfattning : Point process learning is a new statistical theory that gives us a way to estimate parameters using cross-validation for point processes. By thinning a point pattern we are able to create training and validation sets which are then used in prediction errors. LÄS MER
5. MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels in Stacking Ensemble Learning
Kandidat-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