### Table 1. Packet description for the Kohonen Self-Organizing Map implementation.

2002

"... In PAGE 3: ... After having read the sensor values, each unit compares those values with its internal values, stored in the randomly initialised prototype vectors and calculates the Euclidean distance between both vectors. A packet is then created and broadcast across the network with the elements as they are listed in Table1 . The timestamp is provided to eliminate outdated packages.... ..."

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### Table 2 Self-organizing properties of image processing and analysis methods in this work

"... In PAGE 2: ... The results in the present work show that most image processing tasks and a large part of image analysis problems can be solved in parallel structures by self-organizing methods. In Table2 the parameter estimation methods of the image-defined processes and the feature extraction processes through evolving analog processes or series of iterations are detailed. There are other classes of difficult problems, where the image understanding problems can be preceded by some of the methods in Table 1.... ..."

### Table 1. Algorithms for self-organizing modeling

"... In PAGE 2: ... nonparametric models Known nonparametric model selection methods include: Analog Complexing (AC) which selects nonparametric prediction models from a given data set representing one or more patterns of a trajectory of past behavior which are analogous to a chosen reference pattern and Objective Cluster Analysis (OCA). Table1 shows some data mining functions and more appropriate self-organizing modeling algorithms for addressing these functions. Table 1.... ..."

### Table 7: Kohonen self-organizing feature map of the iris data.

in Input Data Coding in Multivariate Data Analysis: Techniques and Practice in Correspondence Analysis

"... In PAGE 11: ..., 1997). Table7 shows a Kohonen map of the original Fisher iris data. The user can trace a curve separating observation sequence numbers less than or equal to 50 (class 1), from 51 to 100 (class 2), and above 101 (class 3).... In PAGE 11: ... The zero values indicate map nodes or units with no assigned observations. The map of Table7 , as for Table 8, has 20 20 units. The number of epochs used in training was in both cases 133.... In PAGE 11: ...ersion of the Fisher data. The user in this case can demarcate class 1 observations. Classes 2 and 3 are more confused, even if large contiguous \islands quot; can be found for both of these classes. The result shown in Table 8 is degraded compared to Table7 . It is not badly degraded though insofar as sub-classes of classes 2 and 3 are found in adjacent and contiguous areas.... ..."

### Table 1. Algorithms for self-organizing modeling (see [19] for such classification) Data Mining functions Algorithm

"... In PAGE 3: ... In a wider sense, the spectrum of self-organizing modeling contains regression-based methods, rule-based methods, symbolic modeling and nonparametric model selection methods. Table1 shows some data mining functions and more appropriate SOM algorithms for addressing these functions (FRI: Fuzzy rule induction using GMDH, AC: Analog Complexing). Table 1.... ..."

### TABLE I DIATOM IDENTIFICATION A) REPORTED CONFUSION MATRIX AND B) SELF-ORGANIZING MODEL CONFUSION MATRIX

2005

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### Table 3: 98% bounds of absolute interference cost (Table 3a: Before Self-Organization)

"... In PAGE 6: ...Table3... ..."

### Table 21: Constant parameters for self-organizing network The epoch size is variable during training, ranging from 1000 patterns in the initial training phases to 8000 patterns during the nal training phases. This approach is taken primarily 62

"... In PAGE 70: ... Based on a similar philosophy, the rule for adapting the neighbourhood size is r(k) = rmin + r0 1 + exp ( r (k ? Nrh)); where r0 4 = initial neighbourhood radius; r 4 = ln ( r= (1 ? r)) = (Nrc ? Nrh) such that at k = Nrc lt; Nrh, r(k) = rmin + r0(1 ? ), and at k = Nrh, r(k) = rmin + r0=2. Table21 gives the constant parameters for the self-organizing network used.... ..."

### Table 1: Mean square error for neuron weights and stan- dard deviation for probability density estimates ( ) 6 Conclusions A new integrally distributed self-organizing learning al- gorithm for the class of neural networks introduced by Kohonen [1] was presented. The algorithm converges to an equiproblable topology preserving map for arbitrarily distributed input signals. It is shown that Kohonen apos;s al- gorthim converges to a locally a ne self-organizing map. Similations results agree with theoretical predictions.

"... In PAGE 5: ... As expected, the results of the three algo- rithms are fairly similar, for the case of a uniformly dis- tributed input signal. Table1 contains the mean square error for the neuron weights and the standard deviation of the probability density estimate vector, ^ p, for both simu- lations.It should be noted that the improvement in perfor- mance comes at an increase in computational cost.... ..."

### TABLE III COMPARISON OF THE MOTIF SETS FROM SELF-ORGANIZING NEURAL NETWORK METHOD AND MEME METHOD, NOS = NUMBER OF SAMPLES IN THE MOTIF SET.

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