Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Local metric adaptation for soft nearest prototype classification to classify proteomic data
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Soft nearest prototype classification
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Matrix Learning for Topographic Neural Maps
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Local matrix adaptation in topographic neural maps
Neurocomputing
Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction
Artificial Intelligence in Medicine
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Proteomic profiling based on mass spectrometry is an important tool for studies at the protein and peptide level in medicine and health care. Thereby, the identification of relevant masses, which are characteristic for specific sample states e.g. a disease state is complicated. Further, the classification accuracy and safety is especially important in medicine. The determination of classification models for such high dimensional clinical data is a complex task. Specific methods, which are robust with respect to the large number of dimensions and fit to clinical needs, are required. In this contribution two such methods for the construction of nearest prototype classifiers are compared in the context of clinical proteomic studies, which are specifically suited to deal with such high-dimensional functional data. Both methods are suitable to the adaptation of the underling metric, which is useful in proteomic research to get a problem adequate representation of the clinical data. In addition they allow fuzzy classification and for one of them allows fuzzy classified training data. Both algorithms are investigated in detail with respect to their specific properties. A performance analyses is taken on real clinical proteomic cancer data in a comparative manner.