213 citations · 629 across the 15 of their papers we have counts for
6 papers · 1 filter
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
Werner Zellinger, Bernhard A. Moser, Thomas Grubinger +3
A novel approach for unsupervised domain adaptation for neural networks is proposed. It relies on metric-based regularization of the learning process. The metric-based regularizati…
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Mahardhika Pratama, Eric Dimla, Edwin Lughofer +2
Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material…
Metacognitive Learning Approach for Online Tool Condition Monitoring
Mahardhika Pratama, Eric Dimla, Chow Yin Lai +1
As manufacturing processes become increasingly automated, so should tool condition monitoring (TCM) as it is impractical to have human workers monitor the state of the tools contin…
Parsimonious Random Vector Functional Link Network for Data Streams
Mahardhika Pratama, Plamen P. Angelov, Edwin Lughofer
The theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of r…
Evolving Ensemble Fuzzy Classifier
Mahardhika Pratama, Witold Pedrycz, Edwin Lughofer
The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than i…
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer +2
The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the d…