Knowledge Transfer Applied on an Anomaly Detection Problem Using Financial Data

Knowledge Transfer Applied on an Anomaly Detection Problem Using Financial Data

Anomaly detection in high-dimensional financial transaction data is challenging and resource-intensive, particularly when the dataset is unlabeled. Sometimes, one can alleviate the computational cost and improve the results by utilizing a pre-trained model, provided that the features learned from th...

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Bibliographic Details
Main Author: Natvig, Filip
Format: Others
Language:English
Published: Uppsala universitet, Avdelningen för systemteknik 2021
Subjects:
Machine learning
Deep learning
Artificial intelligence
AI
Neural networks
Anomaly detection
Transfer learning
Knowledge transfer
LSTM Autoencoder
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451884

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451884

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