Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern

[1]  Thomas Villmann,et al.  Margin based Active Learning for LVQ Networks , 2007, ESANN.

[2]  Thomas Villmann,et al.  Fuzzy classification by fuzzy labeled neural gas , 2006, Neural Networks.

[3]  Thomas Villmann,et al.  Comparison of relevance learning vector quantization with other metric adaptive classification methods , 2006, Neural Networks.

[4]  Alessandro Sperduti,et al.  Unsupervised clustering of continuous trajectories of kinematic trees with SOM-SD , 2006, ESANN.

[5]  Ben J. A. Kröse,et al.  Self-organizing mixture models , 2005, Neurocomputing.

[6]  Thomas Villmann,et al.  Classification using non-standard metrics , 2005, ESANN.

[7]  Barbara Hammer,et al.  Prototype based recognition of splice sites , 2005 .

[8]  Thomas Villmann,et al.  On the Generalization Ability of GRLVQ Networks , 2005, Neural Processing Letters.

[9]  Thomas Villmann,et al.  Supervised Neural Gas with General Similarity Measure , 2005, Neural Processing Letters.

[10]  Alessio Micheli,et al.  Recursive self-organizing network models , 2004, Neural Networks.

[11]  Ah Chung Tsoi,et al.  A supervised self-organizing map for structures , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[12]  Martina Hasenjäger,et al.  Active Learning with Local Models , 1998, Neural Processing Letters.

[13]  Andrew W. Moore,et al.  Locally Weighted Learning for Control , 1997, Artificial Intelligence Review.

[14]  Thomas Villmann,et al.  Neural maps in remote sensing image analysis , 2003, Neural Networks.

[15]  Thomas Villmann,et al.  Mathematical Aspects of Neural Networks , 2003, ESANN.

[16]  B. Hammer,et al.  Monitoring technical systems with prototype based clustering , 2003 .

[17]  Alessio Micheli,et al.  A general framework for unsupervised processing of structured data , 2004, Neurocomputing.

[18]  Thomas Villmann,et al.  Neural maps for faithful data modelling in medicine - state-of-the-art and exemplary applications , 2002, Neurocomputing.

[19]  Panu Somervuo,et al.  How to make large self-organizing maps for nonvectorial data , 2002, Neural Networks.

[20]  Thomas Voegtlin,et al.  Recursive self-organizing maps , 2002, Neural Networks.

[21]  Marc M. Van Hulle,et al.  Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization , 2000 .

[22]  T. Heskes.  Energy functions for self-organizing maps , 1999 .

[23]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[24]  Thomas Villmann,et al.  Topology preservation in self-organizing feature maps: exact definition and measurement , 1997, IEEE Trans. Neural Networks.

[25]  Jukka Heikkonen,et al.  Time Series Predicition using Recurrent SOM with Local Linear Models , 1997 .

[26]  G. Deco,et al.  An Information-Theoretic Approach to Neural Computing , 1997, Perspectives in Neural Computing.

[27]  Atsushi Sato,et al.  Generalized Learning Vector Quantization , 1995, NIPS.

[28]  Dominik R. Dersch,et al.  Asymptotic level density in topological feature maps , 1995, IEEE Trans. Neural Networks.

[29]  Jagat Narain Kapur,et al.  Measures of information and their applications , 1994 .

[30]  Thomas Martinetz,et al.  'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[31]  John G. Taylor,et al.  The temporal Kohönen map , 1993, Neural Networks.

[32]  Helge J. Ritter,et al.  Neural computation and self-organizing maps - an introduction , 1992, Computation and neural systems series.

[33]  Paul L. Zador,et al.  Asymptotic quantization error of continuous signals and the quantization dimension , 1982, IEEE Trans. Inf. Theory.

[34]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[35]  A. Rényi.  On Measures of Entropy and Information , 1961 .