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Title of Paper:Self-Anomaly-Detection Model Training via Initialized Meta Model

Journal:International Conference on Networking and Network Applications

Abstract:Anomaly detection has become a key challenge affecting the training accuracy of machine learning. Because the training data is usually collected from Internet, many noised samples will be captured and these samples can decrease the model training accuracy. However, because the abnormal samples are difficult to predict when the samples are collected, the training samples collected may contain many unknown exception categories, and the labels of normal samples may be incorrect, in this case, it is difficult to train an anomaly detection model based on supervised learning to

All the Authors:Xi Ning

Document Code:10.1109/NaNA56854.2022.00087

Page Number:pages 471-476

Translation or Not:no