Sökning: "parametrar för klassificering"

Visar resultat 1 - 5 av 57 uppsatser innehållade orden parametrar för klassificering.

1. 1. The impact of pruning Convolutional Neural Networks when classifying skin cancer

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustaf Larsson; Marcus Odin; [2023]
   Nyckelord :;

   Sammanfattning : Over the past few years, there have been multiple reports showcasing how Convolutional Neural Networks (CNNs) can be used to classify if skin lesions are cancerous or non-cancerous. However, a limitation of CNNs is the large number of parameters resulting in high computation times. LÄS MER

3. 2. An Evaluation of Classical and Quantum Kernels for Machine Learning Classifiers

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Teo Nordström; Jacob Westergren; [2023]
   Nyckelord :Machine Learning; Quantum Computing; Kernels; Support Vector Machines; Maskininlärning; Kvantberäkning; Kärnor; Stödvektormaskin;

   Sammanfattning : Quantum computing is an emerging field with potential applications in machine learning. This research project aimed to compare the performance of a quantum kernel to that of a classical kernel in machine learning binary classification tasks. LÄS MER

4. 3. Sömnregistrering med hjälp av ActiGraph GT9X Link och Polar Vantage V2 - en jämförande studie

   Uppsats för yrkesexamina på grundnivå, Uppsala universitet/Pediatrisk inflammations- och metabolismforskning samt barnhälsa

   Författare :Joel Korpi; Sam Torkamani; [2023]
   Nyckelord :ActiGraph GT9X Link; Polar Vantage V2; sleep trackers; sleep; ActiGraph GT9X Link; Polar Vantage V2; sömnmätare; sömn;

   Sammanfattning : Background:  In recent years the interest in self-care has expanded, with sleep playing a big part. Concurrently, the development of self-monitoring wristwatches has enabled individuals to track their sleep. Commonly these are called sleep trackers. LÄS MER

5. 4. Comparison of CNN and LSTM for classifying short musical samples

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Tore Nylén; Victor Stenmark; [2023]
   Nyckelord :;

   Sammanfattning : Applying machine learning to music and audio data is becoming increasingly common. One such area of research is instrument classification, which is the task of identifying the instrument played in a given audio file. In this study, we compared two machine learning model types, LSTM and CNN, on the task of classifying ten different instruments. LÄS MER

7. 5. Computer-Aided Characterization of Lung - Segmentation and Vessel Tree Analysis Algorithms for Clinical Research Applications

   Master-uppsats, KTH/Fysik

   Författare :Daniel Karoumi; [2023]
   Nyckelord :Lung; lung segmentation; NSIP; IPF; lung vessels; lung morphometry; 3D Slicer; Lunga; lungsegmentering; NSIP; IPF; lungkärl; lung morfometri; 3D Slicer;

   Sammanfattning : The initial stage of a lung examination involves the segmentation of a CT image, a process that has been put under a lot of pressure with the high demand for chest scans and accurate segmentations. Current automatic segmentation algorithms are either non-robust for different datasets, not easily accessible, or time-consuming. LÄS MER