World's Best Scientists 2026 revealed!
Barbara Hammer

Barbara Hammer

D-Index & Metrics

Computer Science

D-Index
47
Citations
10398
World Ranking
6424
National Ranking
300

Overview

Barbara Hammer is affiliated with Bielefeld University in Germany and has contributed extensively to the field of computer science, particularly focusing on artificial intelligence and related subfields.

Their research output includes 276 publications, with a significant emphasis on artificial intelligence, accumulating 215 works in this subfield. Other areas of interest include computer vision and pattern recognition, management science and operations research, civil and structural engineering, and signal processing.

Barbara Hammer's main topics of work cover a range of specialized areas such as:

  • Data Stream Mining Techniques
  • Anomaly Detection Techniques and Applications
  • Machine Learning and Data Classification
  • Explainable Artificial Intelligence (XAI)
  • Water Systems and Optimization
  • Advanced Bandit Algorithms Research
  • Adversarial Robustness in Machine Learning

Their recent papers demonstrate a focus on explainability, concept drift, and robust learning in evolving environments. Notable recent publications include:

  • "Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems" (2020), published in IEEE Transactions on Cognitive and Developmental Systems
  • "Model-based explanations of concept drift" (2023), published in Neurocomputing
  • "One or two things we know about concept drift-a survey on monitoring in evolving environments. Part A: detecting concept drift" (2024), published in Frontiers in Artificial Intelligence
  • "Incremental permutation feature importance (iPFI): towards online explanations on data streams" (2023), published in Machine Learning
  • "Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning" (2021), published in Neural Networks

Barbara Hammer has frequently collaborated with several coauthors throughout their career. The most recurrent collaborators are:

  • André Artelt
  • Fabian Hinder
  • Valerie Vaquet
  • Eyke Hüllermeier
  • Johannes Brinkrolf

With publications appearing frequently in venues such as arXiv (Cornell University), Neurocomputing, the IEEE Symposium Series on Computational Intelligence (SSCI), ESANN proceedings, and Zenodo (CERN European Organization for Nuclear Research), Barbara Hammer has established a broad presence across multiple reputable conferences and journals.

In addition to journal and conference papers, Barbara Hammer has contributed to book publications, notably with Springer Science+Business Media, including titles in the "Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track" series in 2021.

Best Publications

  • Generalized relevance learning vector quantization

    Barbara Hammer;Thomas Villmann

  • Adaptive relevance matrices in learning vector quantization

    Petra Schneider;Michael Biehl;Barbara Hammer

  • Incremental on-line learning: A review and comparison of state of the art algorithms

    Viktor Losing;Viktor Losing;Barbara Hammer;Heiko Wersing

  • Incremental learning algorithms and applications

    Alexander Gepperth;Barbara Hammer

  • Parametric nonlinear dimensionality reduction using kernel t-SNE

    Andrej Gisbrecht;Alexander Schulz;Barbara Hammer

  • KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift

    Viktor Losing;Barbara Hammer;Heiko Wersing

  • Neural maps in remote sensing image analysis

    Thomas Villmann;Erzsébet Merényi;Barbara Hammer

  • Supervised Neural Gas with General Similarity Measure

    Barbara Hammer;Marc Strickert;Thomas Villmann

  • Merge SOM for temporal data

    Marc Strickert;Barbara Hammer

  • Batch and median neural gas

    Marie Cottrell;Barbara Hammer;Alexander Hasenfuß;Thomas Villmann

  • On the approximation capability of recurrent neural networks

    Barbara Hammer

  • Prototype-based models in machine learning.

    Michael Biehl;Barbara Hammer;Thomas Villmann

  • Recursive self-organizing network models

    Barbara Hammer;Alessio Micheli;Alessandro Sperduti;Marc Strickert

  • Limited Rank Matrix Learning, discriminative dimension reduction and visualization

    Kerstin Bunte;Petra Schneider;Barbara Hammer;Frank-Michael Schleif

  • A Note on the Universal Approximation Capability of Support Vector Machines

    Barbara Hammer;Kai Gersmann

  • Distance learning in discriminative vector quantization

    Petra Schneider;Michael Biehl;Barbara Hammer

  • A general framework for unsupervised processing of structured data

    Barbara Hammer;Alessio Micheli;Alessandro Sperduti;Marc Strickert

  • Dynamics and Generalization Ability of LVQ Algorithms

    Michael Biehl;Anarta Ghosh;Barbara Hammer

  • A general framework for dimensionality-reducing data visualization mapping

    Kerstin Bunte;Michael Biehl;Barbara Hammer

  • Topographic mapping of large dissimilarity data sets

    Barbara Hammer;Alexander Hasenfuss

  • Neural Smithing --- Supervised Learning in Feedforward Artificial Neural Networks

    Barbara Hammer

Frequent Co-Authors

Thomas Villmann
Thomas Villmann Hochschule Mittweida
Alexander Schulz
Alexander Schulz University of Copenhagen
Alessio Micheli
Alessio Micheli University of Pisa
Alessandro Sperduti
Alessandro Sperduti University of Padua
Peter Tino
Peter Tino University of Birmingham
Michel Verleysen
Michel Verleysen Université Catholique de Louvain
Pascal Hitzler
Pascal Hitzler Kansas State University
Jochen J. Steil
Jochen J. Steil Technische Universität Braunschweig
Mario Botsch
Mario Botsch TU Dortmund University
Axel Wismüller
Axel Wismüller University of Rochester

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