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Hittade 3 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Improving Recommender Engines for Video Streaming Platforms with RNNs and Multivariate Data

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

    Författare :Daniel Pérez Felipe; [2022]
    Nyckelord :Recurrent neural networks; Recommender systems; Video on demand; Clustering methods; Återkommande neurala nätverk; Rekommendationssystem; Video på begäran; Klustermetoder; Redes neuronales recurrentes; Sistemas de recomendación; Vídeo bajo demanda; Métodos de clustering;

    Sammanfattning : For over 4 years now, there has been a fierce fight for staying ahead in the so-called ”Streaming War”. The Covid-19 pandemic and its consequent confinement only worsened the situation. In such a market where the user is faced with too many streaming video services to choose from, retaining customers becomes a necessary must. LÄS MER

  3. 2. Clustering Methods as a Recruitment Tool for Smaller Companies

    Master-uppsats, KTH/Matematisk statistik

    Författare :Linnea Thorstensson; [2020]
    Nyckelord :Statistics; Clustering; Mapper; K-means clustering; Hierarchical clustering; Principal component analysis; recruitment; Statistik; Klustermetoder; PCA; rekrytering;

    Sammanfattning : With the help of new technology it has become much easier to apply for a job. Reaching out to a larger audience also results in a lot of more applications to consider when hiring for a new position. This has resulted in that many big companies uses statistical learning methods as a tool in the first step of the recruiting process. LÄS MER

  4. 3. Clustering Based Outlier Detection for Improved Situation Awareness within Air Traffic Control

    Master-uppsats, KTH/Optimeringslära och systemteori

    Författare :Hanna Gustavsson; [2019]
    Nyckelord :Applied Mathematics; Clustering; Spectral Clustering; Graph Theory; GMM; Outlier Detection; Tillämpad matematik; Klustering; Spektralklustering; grafteori; GMM; anomalidetektering;

    Sammanfattning : The aim of this thesis is to examine clustering based outlier detection algorithms on their ability to detect abnormal events in flight traffic. A nominal model is trained on a data-set containing only flights which are labeled as normal. LÄS MER