Sökning: "unlabeled"

Visar resultat 1 - 5 av 89 uppsatser innehållade ordet unlabeled.

  1. 1. Active learning for text classification in cyber security

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

    Författare :Amanda Carp; [2023]
    Nyckelord :Interactive machine learning; Active learning; Cost-effective active learning; Cyber environment; Interaktiv maskininlärning; Aktiv inlärning; Kostnadseffektiv aktiv inlärning; Cyberdomänen;

    Sammanfattning : In the domain of cyber security, machine learning promises advanced threat detection. However, the volume of available unlabeled data poses challenges for efficient data management. This study investigates the potential for active learning, a subset of interactive machine learning, to reduce the effort required for manual data labelling. LÄS MER

  2. 2. Exploring Advanced Clustering Techniques for Business Descriptions : A Comparative Study and Analysis of DBSCAN, K-Means, and Hierarchical Clustering

    Uppsats för yrkesexamina på avancerad nivå, Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

    Författare :Wisam Orabi Alkhen; [2023]
    Nyckelord :Machine learning; Business descriptions; Search scope reduction; Relevant business terminology; Data analysis.;

    Sammanfattning : In this study, we introduce several approaches to analyze large volumes of business descriptions by applying machine learning clustering and classification algorithms. The goal is to efficiently classify these descriptions, reducing the search scope and allowing for better business insights and decision-making processes. LÄS MER

  3. 3. Classification of Radar Emitters using Semi-Supervised Contrastive Learning

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

    Författare :Tim Jonsson; [2023]
    Nyckelord :;

    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

  4. 4. Semi-Supervised Head Detection for Low Resolution Images

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

    Författare :Annie Biby Rapheal; [2023]
    Nyckelord :Object detection; Semi Supervised Learning; Head detection; Objektdetektion; Semisupervised Learning; Huvuddetektion;

    Sammanfattning : Object detection is a widely researched and applied field in computer vision. Deep learning models have successfully been used for object detection over the years. The performance of State of the art (SOTA) object detection deep learning models is dependent on the number of labeled images. LÄS MER

  5. 5. Improving Image Classification using Domain Adaptation for Autonomous Driving : A Master Thesis in Collaboration with Scania

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

    Författare :Mikael Westlund; [2023]
    Nyckelord :Autonomous driving; Deep Learning; Domain Adaptation; Image classification; LiDAR; Transfer Learning; Självkörande fordon; Djupinlärning; Domain Adaptation; Bildklassificering; LiDAR; Överföringsinlärning;

    Sammanfattning : Autonomous driving is a rapidly changing industry and has recently become a heavily focused research topic for vehicle producing companies and research organizations. These autonomous vehicles are typically equipped with sensors such as Light Detection and Radar (LiDAR) in order to perceive their surroundings. LÄS MER