Sökning: "Supervised Methods"
Visar resultat 6 - 10 av 369 uppsatser innehållade orden Supervised Methods.
6. Analyzing the performance of active learning strategies on machine learning problems
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknikSammanfattning : Digitalisation within industries is rapidly advancing and data possibilities are growing daily. Machine learning models need a large amount of data that are well-annotated for good performance. To get well-annotated data, an expert is needed, which is expensive, and the annotation itself could be very time-consuming. LÄS MER
7. Exploring the Applications of Machine Learning in the Public Sector
Kandidat-uppsats, Lunds universitet/Fysiska institutionen; Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationSammanfattning : Despite the many use cases for machine learning, it sees minimal usage in Sweden’s public sector today. It is important that the public sector in particular utilizes the most efficient tools available. LÄS MER
8. Learning Embeddings for Fashion Images
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : Today the process of sorting second-hand clothes and textiles is mostly manual. In this master’s thesis, methods for automating this process as well as improving the manual sorting process have been investigated. LÄS MER
9. Analyzing How Blended Emotions are Expressed using Machine Learning Methods
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Blended emotion is a classification of emotional experiences that involve the combination of multiple emotions. Research on the expression of blended emotions allows researchers to understand how different emotions interact and coexist in an individual’s emotional experience. LÄS MER
10. Understanding the Robustnessof Self Supervised Representations
Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknikSammanfattning : This work investigates the robustness of learned representations of self-supervised learn-ing approaches, focusing on distribution shifts in computer vision. Joint embedding architecture and method-based self-supervised learning approaches have shown advancesin learning representations in a label-free manner and efficient knowledge transfer towardreducing human annotation needs. LÄS MER