Edwin Lughofer mostly deals with Artificial intelligence, Machine learning, Fuzzy control system, Fuzzy logic and Data mining. His work on Artificial neural network, Training set and Semantics as part of his general Artificial intelligence study is frequently connected to USable, thereby bridging the divide between different branches of science. His biological study spans a wide range of topics, including Classifier and System identification.
His Fuzzy control system course of study focuses on Interpretability and Anomaly detection and Visual inspection. His Fuzzy logic research includes elements of Contextual image classification, Nonlinear system and Pattern recognition. Edwin Lughofer studies Data stream mining which is a part of Data mining.
His main research concerns Artificial intelligence, Machine learning, Fuzzy control system, Fuzzy logic and Data mining. His Artificial intelligence study combines topics from a wide range of disciplines, such as Context, Data stream mining and Pattern recognition. His study in the field of Active learning and Incremental learning also crosses realms of Active learning and Field.
The concepts of his Fuzzy control system study are interwoven with issues in Fuzzy set, Interpretability, Mathematical optimization and Curse of dimensionality. His studies in Fuzzy logic integrate themes in fields like Algorithm, Robustness and System identification. As a member of one scientific family, Edwin Lughofer mostly works in the field of Data mining, focusing on Cluster analysis and, on occasion, Vector quantization.
Edwin Lughofer mainly investigates Artificial intelligence, Artificial neural network, Fuzzy control system, Data stream mining and Machine learning. His Artificial intelligence research includes themes of Flexibility, Data science and Pattern recognition. His study in Artificial neural network is interdisciplinary in nature, drawing from both Nonlinear system, Trajectory, Fuzzy logic and Big data.
His work on Fuzzy set as part of general Fuzzy logic study is frequently linked to Data modeling and Steering wheel, bridging the gap between disciplines. His research in Fuzzy control system intersects with topics in Data stream, Algorithm and Pruning. When carried out as part of a general Data stream mining research project, his work on Concept drift is frequently linked to work in Scatternet, therefore connecting diverse disciplines of study.
His scientific interests lie mostly in Artificial intelligence, Artificial neural network, Condition monitoring, Pattern recognition and Regression analysis. His studies in Artificial intelligence integrate themes in fields like Data stream mining, Concept drift and Reduction. The Artificial neural network study combines topics in areas such as Control system, Control theory, Sliding mode control, PID controller and Big data.
His Condition monitoring research incorporates themes from Physical model, Wiener process and Robustness. His Pattern recognition study combines topics in areas such as Data stream, Denoising autoencoder and Flexibility. Edwin Lughofer interconnects Transfer of learning, Data mining, Specific-information and Domain knowledge in the investigation of issues within Regression analysis.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems (2008)
PANFIS: A Novel Incremental Learning Machine
Mahardhika Pratama;Sreenatha G. Anavatti;Plamen P. Angelov;Edwin Lughofer.
IEEE Transactions on Neural Networks (2014)
Evolving fuzzy classifiers using different model architectures
P. Angelov;E. Lughofer;X. Zhou.
Fuzzy Sets and Systems (2008)
Extensions of vector quantization for incremental clustering
Pattern Recognition (2008)
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
E. Lughofer;P. Angelov.
soft computing (2011)
Learning in Non-Stationary Environments: Methods and Applications
Moamar Sayed-Mouchaweh;Edwin Lughofer.
GENEFIS: Toward an Effective Localist Network
Mahardhika Pratama;Sreenatha G. Anavatti;Edwin Lughofer.
IEEE Transactions on Fuzzy Systems (2014)
Generalized smart evolving fuzzy systems
Edwin Lughofer;Carlos Cernuda;Stefan Kindermann;Mahardhika Pratama.
Evolving Systems (2015)
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
Werner Zellinger;Thomas Grubinger;Edwin Lughofer;Thomas Natschläger.
arXiv: Machine Learning (2017)
Profile was last updated on December 6th, 2021.
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