- The dynamics of Learning Vector Quantization Michael Biehl1
- Causal Relevance Learning for Robust Classification under Interventions
- Statistical Mechanics of Unsupervised Structure Recognition
- Universit at W urzburg Institut f ur Theoretische Physik
- Algemeen Secretariaat Esmee Elshof
- Universit at W urzburg Institut f ur Theoretische Physik
- Terrace Sizes and Particle Currents in Epitaxial Growth Michael BIEHL and Wolfgang KINZEL
- On--line Learning with a Perceptron Michael Biehl \Lambda
- Kinetic model of II-VI(001) semiconductor surfaces: Growth rates in atomic layer epitaxy T. Volkmann, M. Ahr, and M. Biehl
- Adaptive Metrics for Content Based Image Retrieval in Dermatology
- Universit at W urzburg Institut f ur Theoretische Physik
- Unconventional MBE Strategies from Computer Simulations for Optimized Growth Conditions
- A lattice gas model of II{VI(001) semiconductor M. Biehl 1;2 , M. Ahr 1 , W. Kinzel 1 , M. Sokolowski 2;3 and T. Volkmann 1
- Divergence based classification in Learning Vector Quantization
- Universit at W urzburg Institut f ur Theoretische Physik
- Simulation of heteroepitaxial growth and misfit dislocations
- Kinetic model of IIVI(001) semiconductor surfaces: Growth rates in atomic layer epitaxy T. Volkmann, M. Ahr, and M. Biehl
- Simulation of wetting-layer and island formation in heteroepitaxial growth
- Adaptive Local Dissimilarity Measures for Discriminative Dimension Reduction of Labeled Data
- Window-based example selection in learning vector quantization
- Learning of Causal Relations John A. Quinn1
- Regularization in Matrix Relevance Learning Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann and Michael Biehl
- Adaptive relevance matrices in Learning Vector Quantization
- Phase Transitions in Vector Quantization and Neural Gas Aree Witoelar a, Michael Biehl a
- Statistical Mechanics of On-line learning Michael Biehl1
- Hyperparameter Learning in Robust Soft LVQ Petra Schneider1
- Learning Dynamics and Robustness of Vector Quantization and Neural Gas
- Simulation of self-assembled nanopatterns in strained 2D alloys on the fcc(111) surface
- On the Dynamics of Vector Quantization and Neural Gas
- Performance Analysis of LVQ Algorithms: A Statistical Physics Approach
- Efficient training of multilayer perceptrons using principal component analysis Christoph Bunzmann1
- Nano '03 Brno 2003 MECHANISM OF FORMATION OF SELF-ASSEMBLED
- Algemeen Secretariaat Esmee Elshof
- Neural Models of Recurrent Processing in Form and Motion Perception Heiko Neumann
- Nonlinear Discriminative Data Visualization Kerstin Bunte1
- An exactly solvable model of unsupervised Michael Biehl \Lambda
- Singularity spectra of rough growing surfaces from wavelet M. Ahr , M. Biehl
- On--line Learning of Prototypes and Principal Components
- Relevance Matrices in LVQ Petra Schneider1
- Learning from examples in Neural Gas and Vector Quantization Michael Biehl, Anarta Ghosh, and Aree Witoelaar
- Universit at W urzburg Institut f ur Theoretische Physik
- Universit at W urzburg Institut f ur Theoretische Physik
- Training multilayer perceptrons by principal component analysis
- Kinetic Monte Carlo Simulations of dislocations in heteroepitaxial growth
- Universit at W urzburg Institut f ur Theoretische Physik
- Invited contribution to the NATO Advanced Research Workshop on Quantum Dots: Fundamentals, Applications, and Frontiers, June 2003,
- Learning by Online Gradient Descent Michael Biehl \Lambda
- Statistical Mechanics of On--line Learning and Generalization
- Universit at W urzburg Institut f ur Theoretische Physik
- Combined clustering models for the analysis of gene expression of prokaryotic M. Angelova
- Learnability of Periodic Activation Functions General Results Michal Rosen--Zvi 1 , Michael Biehl 2 , and Ido Kanter 1
- Distance learning in discriminative vector quantization
- Learning Vector Quantization: generalization ability and dynamics of competing prototypes
- Classification of Boar Sperm Head Images using Learning Vector Quantization
- Supervised dimension reduction mappings Kerstin Bunte1
- Modelling sublimation and atomic layer epitaxy in the presence of competing surface reconstructions
- Universit at W urzburg Institut f ur Theoretische Physik
- Universit at W urzburg Institut f ur Theoretische Physik
- Matrix Learning in Learning Vector Quantization
- Lattice gas models and Kinetic Monte Carlo simulations of epitaxial growth
- Particle currents and the distribution of terrace sizes in unstable epitaxial growth M. Biehl 1;2 , M. Ahr 1 , M. Kinne 3 , W. Kinzel 1 , and S. Schinzer 1;2
- Supervised learning from clustered input Carmela Marangi
- Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality
- 1. Metric Learning for Prototype-based classification
- ADAPTIVE MATRIX METRICS FOR ATTRIBUTE DEPENDENCE ANALYSIS IN DIFFERENTIAL HIGH-THROUGHPUT DATA
- Modelling (001) surfaces of II-VI semiconductors M. Ahr M. Biehl T. Volkmann
- Divergence based Learning Vector Quantization E. Mwebaze1
- Multivariate class labeling in Robust Soft LVQ Petra Schneider1
- Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data
- Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data
- Dynamical analysis of LVQ type learning rules Anarta Ghosh1,3, Michael Biehl1 and Barbara Hammer2
- Michael Biehl --Publications 1 I. Journal Articles
- Simulation of heteroepitaxial growth and surface alloy formation
- OFF--LINE LEARNING FROM CLUSTERED INPUT EXAMPLES CARMELA MARANGI
- Evaporation and Step Edge Diffusion in MBE S. Schinzer a;? , M. Sokolowski b , M. Biehl a , and W. Kinzel a
- HDTHEP0002 WUEITP2000007
- Invited contribution to the NATO Advanced Research Workshop on Quantum Dots: Fundamentals, Applications, and Frontiers, June 2003,
- Multi-Valued and Universal Binary Neurons
- Equilibrium properties of offline LVQ Aree Witoelar1
- Dynamics and generalization ability of LVQ algorithms Dynamics and generalization ability of LVQ algorithms
- Learning Vector Quantization: The Dynamics of Winner-Takes-All Algorithms
- December 8, 1998 Optimisation of on--line principal component
- Weight decay induced phase transitions in multilayer neural networks
- Noisy regression and classification with continuous multilayer networks
- Hyperparameter Learning in Probabilistic Prototype-based models
- Universit at W urzburg Institut f ur Theoretische Physik
- The role of step edge diffusion in epitaxial crystal growth S. Schinzer ? , M. Kinne, M. Biehl, and W. Kinzel
- Universit at W urzburg Institut f ur Theoretische Physik
- Learning multilayer perceptrons eciently C. Bunzmann, M. Biehl , R. Urbanczik
- , A. Freking, M. Biehl, and K. E. Linsenmair1 Department of Animal Ecology and Tropical Biology, Biozentrum, Am Hubland,
- Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning
- The in uence of the crystal lattice on coarsening in unstable epitaxial growth
- Statistical physics of learning: Phase transitions in multilayered neural networks
- O -lattice Kinetic Monte Carlo simulations of strained heteroepitaxial growth
- Factoring large numbers: easy or hard? Herman te Riele, CWI Amsterdam
- Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ
- Formation and consequences of misfit dislocations in heteroepitaxial growth
- Generalized functional relevance learning vector quantization
- Flat (001) surfaces of II-VI semiconductors: A lattice gas Martin Ahr , Michael Biehl
- A general framework for dimensionality reducing data visualization mapping
- Limited Rank Matrix Learning, Discriminative Dimension Reduction and Visualization
- Functional Relevance Learning in Generalized Learning Vector Quantization
- Adaptive learning for complex-valued data Kerstin Bunte1
- Adaptive Matrices for Color Texture Classification
- Stochastic Neighbor Embedding (SNE) for Dimension Reduction and Visualization using arbitrary Divergences
- Visualizing the quality of dimensionality reduction
- Matrix Relevance LVQ in Steroid Metabolomics Based Classification of Adrenal Tumors
