[HTML][HTML] From concept drift to model degradation: An overview on performance-aware drift detectors

F Bayram, BS Ahmed, A Kassler - Knowledge-Based Systems, 2022 - Elsevier
The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Classification of distribution power grid structures using inception v3 deep neural network

SF Stefenon, KC Yow, A Nied, LH Meyer - Electrical Engineering, 2022 - Springer
To maintain the supply of electrical energy, it is necessary that failures in the distribution grid
are identified during inspections of the electrical power system before shutdowns occur. To …

To regularize or not: Revisiting SGD with simple algorithms and experimental studies

W He, Y Liu - Expert Systems with Applications, 2018 - Elsevier
Abstract Stochastic Gradient Descent (SGD) is one of the most popular first-order methods to
solve optimization problems in large-scale, which has also been widely studied in …

Online Learning in Varying Feature Spaces with Informative Variation

P Qin, L Song - International Conference on Intelligent Information …, 2024 - Springer
Most conventional literature on online learning implicitly assumes a static feature space.
However, in real-world applications, the feature space may vary over time due to the …

A novel sentiment classification model based on online learning

N Qiu, Z Shen, X Hu, P Wang - Journal of Algorithms & …, 2019 - journals.sagepub.com
Memory limitation and slow training speed are two important problems in sentiment analysis.
In this paper, we propose a sentiment classification model based on online learning to …

Frequency analysis and online learning in malware detection

NA Huynh - 2019 - dr.ntu.edu.sg
Traditional antivirus products are signature-based solutions, which rely on a static database
to perform detection. The weakness of this design is that the signatures may become …