### 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 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 The timestamp is provided to eliminate outdated packages.... ..."

Cited by 9

### 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 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 (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

Cited by 2

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

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

### TABLE I THE EFFECT OF NETWORK SIZE FOR THE SELF-ORGANIZING MAP (SOM) The SOM size versus detection results for three different sizes

### Table 3: Error rate of the face recognition system with varying number of dimensions in the self-organizing map. Each result given is the average of three simulations.

1997

Cited by 103

### Table 3: Error rate of the face recognition system with varying number of dimensions in the self-organizing map. Each result given is the average of three simulations.

1997

Cited by 103