Sökning: "machine learning"
Visar resultat 1 - 5 av 353 avhandlingar innehållade orden machine learning.
1. Modularization of the Learning Architecture Supporting Learning Theories by Learning TechnologiesDetta är en avhandling från Stockholm : KTH
Sammanfattning : This thesis explores the role of modularity for achieving a better adaptation of learning technology to pedagogical requirements. In order to examine the interrelations that occur between pedagogy and computer science, a theoretical framework rooted in both fields is applied. LÄS MER
- Detta är en avhandling från Stockholm : Department of Computer and Systems Sciences, Stockholm University
Sammanfattning : Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. LÄS MER
- Detta är en avhandling från Gothenburg : Chalmers tekniska högskola
Sammanfattning : Artificial neural networks have obtained astonishing results in a diverse number of tasks. One of the reasons for the success is their ability to learn the whole task at once (endto-end learning), including the representations for data. LÄS MER
- Detta är en avhandling från Uppsala : Acta Universitatis Upsaliensis
Sammanfattning : Diseases can be caused by foreign agents – pathogens – such as viruses, bacteria and other parasites, entering the body or by an internal malfunction of the body itself. The partial understanding of diseases like cancer and the ones caused by viruses, like the influenza A viruses (IAVs) and the human immunodeficiency virus, means we still do not have an efficient cure or defence against them. LÄS MER
- Detta är en avhandling från Centre for Mathematical Sciences, Lund University
Sammanfattning : This thesis deals with manifold learning techniques and their application in gene expression data analysis. Manifold learning is the study of methods that aim to infer geometrical structure from data sampled from manifolds, enabling nonlinear solutions to various machine learning tasks. LÄS MER