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This paper describes the employment of an 'Adaptive Growing Three-Dimensional Self-Organizing Map' for the classification of images. First a short description of growing SOMs is given and the fundamental advantages are mentioned. Then an extension of the original SOM from two to three dimensions with growing feature is presented. By means of some selected(More)
Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a low-dimensional target space. Thereby, the distance relationships in the source are reconstructed in the target space as best as possible according to a given embedding criterion. Here, a new stress function with intuitive properties and a(More)
A correlation-based similarity measure is derived for generalized relevance learning vector quantization (GRLVQ). The resulting GRLVQ-C classifier makes Pearson correlation available in a classification cost framework where data prototypes and global attribute weighting terms are adapted into directions of minimum cost function values. In contrast to the(More)
This paper describes some achievements in the detection of tumor in medical images. A computer system has been designed and developed to recognize the typical features of the " glioblastoma multiforme " in the digital images of the brain. The basic concept is that local textures in the images can reveal the typical " regularities " of the biological(More)
In this work we introduce a method for visualization of fuzzy label information obtained from prototype based fuzzy labeled self-organizing map (FLSOM) for fuzzy classification. FLSOM returns vectors of fuzyy class labels for the prototypes containing class simlarity information. This information is used for apropriate visualization by an adequate,(More)