#gdvgraph

GDV Corona's types 2. The methodology We first had to preprocess all the pictures so that all were of equal size (320 x 240). We then described the pictures with various sets of numerical parameters (attributes) with five different parametrization algorithms (described in more detail in the next section): a) IP (Image Processor – 22 attributes), b) PCA (Principal Component Analysis), c) Association Rules, d) GDV Assistant with some basic GDV parameters, e)GDV Assistant with additional parameters. Therefore we had available 5 different learning sets for two-classes problem: altered (one of Rings, Spots, and Branches) versus non- altered (one of Ia, Ib, Ic) state of consciousness. Some of the sets were used also as six-classes problems (differentiating among all six different types of coronas). We tried to solve some of the above classification tasks by using various machine learning algorithms as implemented in Weka system (Witten and Frank, 2000):   Quinlan's (1993) C4.5 algorithm for generating decision trees;  K-nearest neighbor classifier by Aha, D., and D. Kibler (1991);  Simple Kernel Density classifier;  Naïve Bayesian classifier using estimator classes: Numeric estimator precision valuesare chosen based on analysis of the training data. For this reason, the classifier is not an Updateable Classifier (which in typical usage are initialized with zero training instances, see (John and Langley, 1995));  SMO implements John C. Platt's sequential minimal optimization algorithm for training a support vector classifier using polynomial kernels. It transforms the output of SVM Types Ia, Ib and Ic– normal state of consciousness into probabilities by applying a standard sigmoid function that is not fitted to the data. This implementation globally replaces all missing values and transforms nominal attributes into binary ones (see Platt, 1998; Keerthi et al., 2001);  Neural networks: standard multilayared feedforward neural network with backpropagation of errors learning mechanism (Rumelhart et al., 1986). SMO algorithm can be used only for two-classes problems, while the other algorithms can be used on two-classes and on six-classes problems. Types Branches, Rings and Spots– altered states of consciousness Figure 1: Example coronas for each type. Full text PDF: 2004-Kononenko-altered-coronas References Aha, D., and D. Kibler (1991) "Instance-based learning algorithms", Machine Learning, vol.6, pp. 37-66. R. Agrawal, T. Imielinski, and A. Swami (1993) Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Mangement of Data, pages 207-216, Washington, D.C., 1993. R. Agrawal and R. 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