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The granulation process is illustrated by the decision tree given in Fig. 3.
The steps are illustrated by the decision tree in Fig. 6.
The third step involves manual post-processing of the rules extracted by the decision tree algorithm.
Using the posterior probabilities provided by the decision tree, a bigram prosodic label sequence model was combined to detect pitch accent and boundary tones at the syllable level.
In the training phase, a rule set that has been generated over time by the decision tree classifier is used to define both known and unknown attributes.
This is not unusual, since the classification model provided by the decision tree can serve as an explanatory tool to distinguish between objects of different classes [32].
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The training phase consists of re-estimating HMM models by using the Baum-Welch algorithm after aligning and tying the models by using the decision tree method [12].
k denotes the number of candidates that the client robot finds by using the decision tree.
The object in the green rectangle represents the candidate object classified by using the decision tree from the viewpoint of the client robot.
The server robot will finish searching when the number of candidates is reduced to one or when the number of candidates obtained by using the decision tree is the same between client and server robots (Figure 54○).
By adjusting the decision tree, the server robot, i.e., the robot requested to execute the task, identifies the target even if some primitive representations are not viewpoint-invariant.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com