### 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 3. Parameters of the self-organization algorithm.

in Efficient publish/subscribe through a self-organizing broker overlay and its application to SIENA

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### Table I:. Algorithms for self-organizing modeling

### 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 4: Absolute IC difference before self-organization between sequential (SEQ) and random (RND) initialization.

"... In PAGE 7: ...3.1 Results and Discussion Table4 distinctly shows an improvement in absolute interference cost (IC) reduction across the wireless mesh region for different node densities- This improvement is obtained by using the proposed random initialization algorithm in comparison to the sequential algorithm, which translates to an improvement in the overall capacity. Table 4: Absolute IC difference before self-organization between sequential (SEQ) and random (RND) initialization.... ..."

### Table 1. Performance Benchmarks for the Self-organizing Superimposition (SOS) and the SPE9 Algorithms on the Four Molecules (See Fig. 2) Under Test.

"... In PAGE 5: ... These values serve as indicators of the quality of the generated conformations. The average values for these deviations over the 10,000 gen- erated conformations, along with the computing times, are pro- vided in Table1 for the four molecules under test. One can see from the table that in all cases the SOS algorithm took less time and generated conformations of higher quality (smaller bond and angle deviations) than the SPE algorithm9 did, indicating that the former achieved a faster convergence rate.... In PAGE 5: ... Some randomly chosen 3D conformations generated by the SOS algorithm are shown in Figure 2, which can be visually confirmed to have sensible geometries. Although Table1 indi- cates that the generated conformations still exhibit some devia- tions with respect to the ideal (reference) geometric parameters, the deviations are not severe if one considers the consistency of these reference parameters over different sets of rules. For example, in some rule-based parameter set widely used in con- formational sampling programs13 including SPE,9 all C27 bonds between two carbon atoms have a length of 1.... ..."

### Table 1. Differences from symbolic values to Rule (1). During execution of the algorithm, these values will be self-organized, aiming to represent a cluster of sound decisions about the class.

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### Table 1. Differences from symbolic values to Rule (1). During execution of the algorithm, these values will be self-organized, aiming to represent a cluster of sound decisions about the class.

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### Table 2: The algorithm to produce Kohonen SOMs. The algorithm in Table 2 contains the necessary steps to produce a Kohonen Self-Organizing Map. The neighbourhood function has usually a bell-shaped curve. A common choice for this function would be

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"... In PAGE 10: ...able 1: The leader clustering algorithm. ................................................................. 23 Table2 : The algorithm to produce Kohonen SOMs.... ..."

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### Table 2: The algorithm to produce Kohonen SOMs. The algorithm in Table 2 contains the necessary steps to produce a Kohonen Self-Organizing Map. The neighbourhood function has usually a bell-shaped curve. A common choice for this function would be

"... In PAGE 10: ...able 1: The leader clustering algorithm. ................................................................. 23 Table2 : The algorithm to produce Kohonen SOMs.... ..."