Sökning: "Oövervakat lärande"

Visar resultat 1 - 5 av 16 uppsatser innehållade orden Oövervakat lärande.

1. 1. Discover patterns within train log data using unsupervised learning and network analysis

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

   Författare :Zehua Guo; [2022]
   Nyckelord :Log analysis; Natural language processing; Unsupervised learning; Clustering; Network analysis; Logganalys; Bearbetning av naturligt språk; Oövervakat lärande; Clustering; Nätverksanalys;

   Sammanfattning : With the development of information technology in recent years, log analysis has gradually become a hot research topic. However, manual log analysis requires specialized knowledge and is a time-consuming task. Therefore, more and more researchers are searching for ways to automate log analysis. LÄS MER

3. 2. Matching Sticky Notes Using Latent Representations

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

   Författare :Javier García San Vicent; [2022]
   Nyckelord :Pattern matching; Image matching; Image recognition; Representation learning; Unsupervised learning; Semisupervised learning; Siamese architecture; Deep learning; Transfer learning; Mönstermatchning; Bildmatchning; Bildigenkänning; Representationsinlärning; Oövervakat lärande; Halvövervakat lärande; Siamesisk arkitektur; Djup lärning; Överfört lärande;

   Sammanfattning : his project addresses the issue of accurately identifying repeated images of sticky notes. Due to environmental conditions and the 3D location of the camera, different pictures taken of sticky notes may look distinct enough to be hard to determine if they belong to the same note. LÄS MER

4. 3. Grouping Similar Bug Reports from Crash Dumps with Unsupervised Learning

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

   Författare :Sara Vestergren; [2021]
   Nyckelord :Unsupervised Learning; Bug Report; Duplicate Detection; Clustering; Software Crash; Oövervakad Inlärning; Felrapport; Dublett-detektering; Klustring; Mjukvarukrasch;

   Sammanfattning : Quality software usually means high reliability, which in turn has two main components; the software should provide correctness, which means it should perform the specified task, and robustness in the sense that it should be able to manage unexpected situations. In other words, reliable systems are systems without bugs. LÄS MER

5. 4. EVALUATION OF UNSUPERVISED MACHINE LEARNING MODELS FOR ANOMALY DETECTION IN TIME SERIES SENSOR DATA

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

   Författare :Lorenzo Bracci; Amirhossein Namazi; [2021]
   Nyckelord :Machine learning; Unsupervised learning; Anomaly detection; Time Series data; Maskininlärning; Oövervakat Lärande; Anomalidetektering; tidsseriedata;

   Sammanfattning : With the advancement of the internet of things and the digitization of societies sensor recording time series data can be found in an always increasing number of places including among other proximity sensors on cars, temperature sensors in manufacturing plants and motion sensors inside smart homes. This always increasing reliability of society on these devices lead to a need for detecting unusual behaviour which could be caused by malfunctioning of the sensor or by the detection of an uncommon event. LÄS MER

7. 5. Depth Estimation from Images using Dense Camera-Lidar Correspondences and Deep Learning

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

   Författare :Ajinkya Khoche; [2020]
   Nyckelord :;

   Sammanfattning : Depth estimation from 2D images is a fundamental problem in Computer Vision, and is increasingly becoming an important topic for Autonomous Driving. A lot of research is driven by innovations in Convolutional Neural Networks, which efficiently encode low as well as high level image features and are able to fuse them to find accurate pixel correspondences and learn the scale of the objects. LÄS MER