GTM: the generative topographic mapping
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition
Neural Computing and Applications
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
Empirical analysis of support vector machine ensemble classifiers
Expert Systems with Applications: An International Journal
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
Categorizing cells in phytoplankton images
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
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Abstract: The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into ''break'' and ''non-break'' classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%.