Essays about: "Semi- Supervised Learning"

Showing result 21 - 25 of 85 essays containing the words Semi- Supervised Learning.

  1. 21. Training a computer vision model using semi-supervised learning and applying post-training quantizations

    University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Author : Albernn Vedin; [2022]
    Keywords : Machine learning; Object detection; Computer vision; Neural network; Semi-supervised learning; Post-training quantizations; Embedded systems; Electrical scooters;

    Abstract : Electrical scooters have gained a lot of attention and popularity among commuters all around the world since they entered the market. After all, electrical scooters have shown to be efficient and cost-effective mode of transportation for commuters and travelers. READ MORE

  3. 22. Sequential Anomaly Detection for Log Data Using Deep Learning

    University essay from Göteborgs universitet/Institutionen för matematiska vetenskaper

    Author : Lina Hammargren; Wei Wu; [2021-06-14]
    Keywords : anomaly detection; recurrent neural network; long short-term memory; semi-supervised learning; seq2seq; transformer; unsupervised learning; log analysis;

    Abstract : Abstract Software development with continuous integration changes needs frequent testing for assessment. Analyzing the test output manually is time-consuming and automating this process could be beneficial to an organization. READ MORE

  4. 23. Application of Machine Learning Algorithms for Post Processing of Reference Sensors

    University essay from Göteborgs universitet/Institutionen för data- och informationsteknik

    Author : VASILIKI LAMPROUSI; [2021-04-01]
    Keywords : Object detection; machine learning; camera; sensors; semi-supervised learning;

    Abstract : The Autonomous Drive (AD) systems and Advanced Driver Assistance Systems (ADAS) in the current and future generations of vehicles include a large number of sensors which are used to perceive the vehicle’s surroundings. The production sensors of these vehicles are verified and validated against reference data that are originated from high-accurate reference sensors that are placed in a reference roof box at the top of the vehicle. READ MORE

  5. 24. Carta ex Machina: Testing object-based machine learning and unsupervised classification in land use change detection mapping in the semi-arid governorate of Sidi Bouzid, Tunisia

    University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

    Author : Kristian Emil Havnsgaard Paludan; [2021]
    Keywords : Change detection; Land use mapping; LANDSAT MSS; LANDSAT TM; GEOBIA; Random Forest classification; ISODATA cluster classification; Object-based classification; Semi-arid agriculture; Irrigation mapping; Earth and Environmental Sciences;

    Abstract : Sidi Bouzid, Tunisia is an inland governorate in Tunisia that has undergone a rapid agricultural and urban development since the Tunisian independence in 1952 from being a rural and largely nomadic region into a hub of irrigated agriculture. In 2010 Mohamed Bouazizi sparked the Tunisian revolution by lighting himself on fire int he city of Sidi Bouzid, with some blaming the inequality and water scarcity created by this rapid expansion in the irrigation farming as an important cause (Bayat, 2017; Malka, 2018). READ MORE

  6. 25. Style Transfer Paraphrasing for Consistency Training in Sentiment Classification

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Núria Casals; [2021]
    Keywords : Semi-Supervised Learning; Data Augmentation; Sentiment Classification; Neural Paraphrasing; Semi-övervakad inlärning; Data förändring; Sentimentklassificering; Neural parafrasering;

    Abstract : Text data is easy to retrieve but often expensive to classify, which is why labeled textual data is a resource often lacking in quantity. However, the use of labeled data is crucial in supervised tasks such as text classification, but semi-supervised learning algorithms have shown that the use of unlabeled data during training has the potential to improve model performance, even in comparison to a fully supervised setting. READ MORE