Sökning: "Erik Rosvall"
Hittade 5 uppsatser innehållade orden Erik Rosvall.
1. Feature Selection for Microarray Data via Stochastic Approximation
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : This thesis explores the challenge of feature selection (FS) in machine learning, which involves reducing the dimensionality of data. The selection of a relevant subset of features from a larger pool has demonstrated its effectiveness in enhancing the performance of various machine learning algorithms. LÄS MER
2. Kommunal IT-säkerhet under en pandemi : Distansarbetets påverkan på informationssäkerhet
Kandidat-uppsats,Sammanfattning : Efter pandemins intåg i världen år 2020 har de flesta människor tvingats ändra sina levnadsvanor, både vad gäller privatliv som arbetsliv. I Sverige har anställda, där tjänsten tillåter, tvingats utföra sina arbetsuppgifter på distans och detta har oftast skett i det egna hemmet. Detta öppnar upp för en hel del frågor. LÄS MER
3. Comparison of sequence classification techniques with BERT for named entity recognition
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This thesis takes its starting point from the recent advances in Natural Language Processing being developed upon the Transformer model. One of the significant developments recently was the release of a deep bidirectional encoder called BERT that broke several state of the art results at its release. LÄS MER
4. Extreme Kernel Machine
Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)Sammanfattning : The purpose of this report is to examine the combinationof an Extreme Learning Machine (ELM) with the KernelMethod. Kernels lies at the core of Support Vector Machines successin classifying non-linearly separable datasets. The hypothesisis that by combining ELM with a kernel we will utilize featuresin the ELM-space otherwise unused. LÄS MER
5. Extreme Kernel Machine
Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)Sammanfattning : The purpose of this report is to examine the combination of an Extreme Learning Machine (ELM) with the Kernel Method . Kernels lies at the core of Support Vector Machines success in classifying non-linearly separable datasets. The hypothesis is that by combining ELM with a kernel we will utilize features in the ELM-space otherwise unused. LÄS MER