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The maximum resolution level is the depth of the tree.
That is, the number of the binary test increases with increasing the depth of the tree.
The index length of each initial codeword determines the depth of the tree.
Finally, a new error function based on the depth of the tree is introduced to reduce perceptron training time.
For efficiency reason, the number of binary tests is determined depend on the depth of the tree.
An example of the distribution of frames at the nodes for a certain depth of the tree is given in Figure 3.
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The model was trained setting the following parameters: numbers of trees = 10, maximum depth of the trees = 12.
The initial population was randomly generated using the ramped half-and-half [14] method with an initial depth of the trees varying between 2 and 6.
One can infer properties of the automaton from the structure of these diagrams, such as the number of components, length of limiting cycles, depth of the trees connecting non-limiting states to these cycles, or symmetries of the diagram.
The depth of the trees was not limited.
When the number of genes per leaf increased, so the depth of the trees was decreased, the ts-RF model obtained better results compared to other models on most cases, as shown in Table 7.
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