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With Gal, Sorin G. ; On the defect of complementarity of fuzzy measures. English summary ; Fuzzy Sets and Systems 131 2002 ; , no. 3, 365380. Wolfgang Sander ; 2003h: 28030 28E10 ; Ban, C. with McEwan, L. J.; N methi, Andr s ; The embedded resolution of f x, y ; English summary ; Studia Sci. Math. Hungar. 38 2001 ; , 5171. Willem Veys ; 2003a: 14016 14E15 ; Ban, Dubravka The Aubert involution and R-groups. English and French summaries ; Ann. Sci. Ecole Norm. Sup. 4 ; 35 2002 ; , no. 5, 673693. Anthony C. Kable ; 2003j: 22019 22E50 van den Ban, Erik with Schlichtkrull, Henrik ; Harmonic analysis on reductive symmetric spaces. English summary ; European Congress of Mathematics, Vol. I Barcelona, 2000 ; , 565582, Progr. Math., 201, Birkh user, Basel, 2001. see 2003b: 00023 ; 43A85 22E99 ; a Ban, Gui Ning with Pang, Sulin; Zhao, Xiao Hai ; The influence of Baer's conditions on groups. Chinese. English and Chinese summaries ; J. Math. Res. Exposition 21 2001 ; , no. 2, 257263. Ji Ping Zhang ; 2003b: 20050 20F16 ; Ban, Jung-Chao with Lin, Song-Sun; Hsu, Cheng-Hsiung ; Spatial disorder of cellular neural networks--with biased term. English summary ; Internat. J. Bifur. Chaos Appl. Sci. Engrg. 12 2002 ; , no. 3, 525534. Alejandro Eduardo Maass ; 2003e: 34082 34C60.
A SOM is a neural network in which the neurons are connected to each other so that they form a regular lattice. Each neuron acts both as an input and output neuron and is associated with a reference vector. The reference vectors are compared with the network's input. The outputs of the neurons depend on how similar the input and reference vectors are. The neuron, reference vector of which is most similar to the input vector, is called the best-matching map unit BMU ; . During the training of the network, the reference vectors of the BMUs and their neighboring neurons are updated so that they better represent the input vectors, in this work the writing style vectors. Due to such training, different neurons will specialize in representing different areas of the input space. In addition, neurons near to each other in the neuron lattice tend to correspond to areas close to each other in the input space. Therefore, a SOM can be seen as a nonlinear mapping from the input space to the lowerdimensional lattice space. The SOM's ability to represent the training data faithfully depends on the true dimensionality of the data set and on the size and dimensionality of the neuron lattice. As the main interest of this work is to find correlations between the writers, all the styles used by only a single writer were omitted from the writing style vectors. So, all the prototypes for which the average similarity was above 0.05 only for a single writer were consider uninteresting. This way, the dimensionality of the writing style vectors was reduced from 2 591 down to 1 764. The kept prototypes were used by 146 subjects on the average. Approximately 11% of the average similarity values were missing from the writing style vectors. The 1 764-dimensional writing style vectors were further analyzed with a SOM in hope of finding interesting structures such as clusters of writers. Various alternatives for the SOM's size, lattice, neighborhood function, training algorithm, training parameter and epochs, initialization, and updating rule were experimented with. Different SOMs were compared with each other by using two quality measures: quantization error and ability to preserve the topology of the data. The former measure is the average distance between each writing style vector and its BMU. The latter one is the proportion of all data vectors for which the first and second BMUs are not adjacent units and cytoxan.

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