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09/18/2023
A Study of Data-driven Methods for Adaptive Forecasting of COVID-19 Cases
Severe acute respiratory disease SARS-CoV-2 has had a found impact on pu...
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05/15/2023
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
In our digital universe nowadays, enormous amount of data are produced i...
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11/23/2022
Unsupervised Unlearning of Concept Drift with Autoencoders
The phenomena of concept drift refers to a change of the data distributi...
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10/13/2022
Data augmentation on-the-fly and active learning in data stream classification
There is an emerging need for predictive models to be trained on-the-fly...
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10/10/2022
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
In real-world applications, the process generating the data might suffer...
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10/03/2022
Nonstationary data stream classification with online active learning and siamese neural networks
We have witnessed in recent years an ever-growing volume of information ...
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09/30/2022
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
Numerous real-world problems from a diverse set of application areas exi...
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10/04/2020
Data-efficient Online Classification with Siamese Networks and Active Learning
An ever increasing volume of data is nowadays becoming available in a st...
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09/24/2020
Online Learning With Adaptive Rebalancing in Nonstationary Environments
An enormous and ever-growing volume of data is nowadays becoming availab...
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03/13/2019
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems
Real-world congestion problems (e.g. traffic congestion) are typically v...
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09/27/2018
Queue-based Resampling for Online Class Imbalance Learning
Online class imbalance learning constitutes a new problem and an emergin...
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01/10/2017
Real-Time Bidding by Reinforcement Learning in Display Advertising
The majority of online display ads are served through real-time bidding ...
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03/16/2016
