- Margin based Active Learning for LVQ Networks
- Magnification control for batch neural gas Barbara Hammer
- Some Complexity Results for Perceptron Networks
- Generalized Relevance LVQ for Time Series Marc Strickert, Thorsten Bojer, Barbara Hammer
- Classification using non-standard metrics Barbara Hammer1
- Relevance determination in learning vector quantization
- Self-Organizing Maps for Time Series Barbara Hammer1, Alessio Micheli2, Nicolas Neubauer3, Alessandro Sperduti4,
- Rule Extraction from SelfOrganizing Networks Barbara Hammer 1 , Andreas Rechtien 1 , Marc Strickert 1 , and Thomas Villmann 2
- IFI TECHNICAL REPORTS Institute of Computer Science,
- Comparison of Relevance Learning Vector Quantization with other Metric Adaptive Classification Methods
- Relevance LVQ versus SVM Barbara Hammer 1 , Marc Strickert 1 , and Thomas Villmann 2
- Learning Vector Quantization for Multimodal Data Barbara Hammer 1 , Marc Strickert 1 , and Thomas Villmann 2
- Generalization of Elman Networks Barbara Hammer
- On Approximate Learning by Multi-layered Feedforward Circuits
- Batch and median neural gas Marie Cottrell
- Neural Networks can approximate Mappings on Structured Objects
- Editorial of the Special issue on Neural Networks and Kernel Methods for Structured
- Mathematical Aspects of Neural Networks
- ARCHITECTURAL BIAS OF RECNNS A mathematical characterization of
- Generalized Relevance LVQ (GRLVQ) with Correlation Measures for
- Architectural Bias in Recurrent Neural Networks -Fractal Analysis
- Estimating Relevant Input Dimensions for Selforganizing Algorithms
- Generalized Relevance Learning Vector Quantization
- Neural networks with small weights implement finite memory machines
- Monitoring technical systems with prototype based clustering
- On the Approximation Capability of Recurrent Neural Networks Barbara Hammer
- Input pruning for neural gas architectures Barbara Hammer 1 and Thomas Villmann 2
- A general framework for unsupervised processing of structured data
- Relevance learning for mental disease classification
- WSOM 2005, Paris Fuzzy Labeled Neural Gas for Fuzzy Classification
- Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern
- Perspectives on Learning Symbolic Data with Connectionistic Systems
- Universal Approximation Capability of Cascade Correlation for Structures
- Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey
- Markovian bias of neural-based architectures with feedback connections
- Prototype based recognition of splice sites Barbara Hammer
- Prototype based Fuzzy Classification in Clinical Proteomics
- Merge SOM for temporal data Marc Strickert
- Unsupervised Recursive Sequence Processing Marc Strickert, Barbara Hammer
- Improving iterative repair strategies for scheduling with the SVM
- Architectural Bias in Recurrent Neural Networks -Fractal Analysis
- Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning Thomas Villmann
- WSOM 2005, Paris Batch neural gas
- Self-Organizing Context Learning Marc Strickert, Barbara Hammer
- Neural Gas for Sequences Marc Strickert and Barbara Hammer
- Neural networks classifying symbolic data Barbara Hammer
- Neural Maps in Remote Sensing Image Analysis Thomas Villmann
- Hardness of Approximation of the Loading Problem for Multi-layered Feedforward Neural Nets
- On the Generalization Ability of Recurrent Networks Barbara Hammer
- Recurrent networks for structured data a unifying approach and its properties
- Improving iterative repair strategies for scheduling with the SVM
- Mapping the Design Space of Reinforcement Learning Problems a Case Study
- (1) Paper no. NNK03085SPI, Recursive self-organizing network models (2) Invited article
