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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)
Seeds are complex structures composed of several maternal and filial tissues which undergo rapid changes during development. In this review, the barley grain is taken as a cereal seed model. Following a brief description of the developing grain, recent progress in grain development modeling is described. 3-D/4-D models based on histological sections or(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)
  • Udo Seiffert
  • 2006
Neural processing of large-scale data sets containing both many input/output variables and a large number of training examples often leads to very large networks. Once these networks become large-scale in the truest sense of the word (several ten thousand weights), two major inconveniences -or possibly a little more than that -occur: (1) conventional(More)