Sökning: "cnn effekt"
Visar resultat 1 - 5 av 12 uppsatser innehållade orden cnn effekt.
1. Classification of Radar Emitters using Semi-Supervised Contrastive Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Radar is a commonly used radio equipment in military and civilian settings for discovering and locating foreign objects. In a military context, pilots being discovered by radar could have fatal consequences. LÄS MER
2. Parameter Estimation of LPI Radar in Noisy Environments using Convolutional Neural Networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Low-probability-of-intercept (LPI) radars are notoriously difficult for electronic support receivers to detect and identify due to their changing radar parameters and low power. Previous work has been done to create autonomous methods that can estimate the parameters of some LPI radar signals, utilizing methods outside of Deep Learning. LÄS MER
3. The impact of Data Augmentation on classification accuracy and training time in Handwritten Character Recognition
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This bachelor thesis was conducted at the Royal Institute of Technology with the purpose of examining several combinations of data augmentation methods and their impact on classification accuracy of a CNN model. Further, the study investigates the time taken to train the model using the different data augmentation methods as to decide which ones have the best impact on classification accuracy in relation to their computational cost. LÄS MER
4. Deep Learning för klassificering av kundsupport-ärenden
Uppsats för yrkesexamina på grundnivå, Högskolan i Gävle/DatavetenskapSammanfattning : Företag och organisationer som tillhandahåller kundsupport via e-post kommer över tid att samla på sig stora mängder textuella data. Tack vare kontinuerliga framsteg inom Machine Learning ökar ständigt möjligheterna att dra nytta av tidigare insamlat data för att effektivisera organisationens framtida supporthantering. LÄS MER
5. A comparative study between MLP and CNN for noise reduction on images : The impact of different input dataset sizes and the impact of different types of noise on performance
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Images damaged by noise present a problem that can be addressed by performing noise-reduction using neural networks. This thesis analyses the performance of two different neural networks, a Mulilayer Perceptron (MLP) and a Convolutional Neural Network (CNN), when performing noise reduction on images. LÄS MER
