Sentence examples for decision tree structures from inspiring English sources
Figure 8 Paired comparison test as a subjective comparison between soft and hard decision tree structures.
Red and blue curves are related to the proposed soft and the conventional hard decision tree structures, respectively.
Nineteen different contextual questions are defined to train the hard decision tree as follows: Figure 3 Samples and decision tree structures.
In order to clarify the soft clustering advantages, a simple sinusoidal regression problem is solved using both soft and hard decision tree structures in this section.
As an obvious consequence of this simple experiment, the hard decision tree structures are not efficient to exploit the continuous attributes (contextual factors), and incorporating the soft decisions in their internal nodes significantly improves their predictive capabilities.
Additionally, the soft decision tree is trained by exploiting four distinct soft questions defined by (25). Figure 3b,c shows the hard and soft decision tree structures trained based on the maximum likelihood decision tree construction algorithms.
Shape features, generated from the HJS-images, and a hierarchical decision tree structure was used for the grading of OA.
Both DALYs37 and costs were combined with the outputs of the epidemiological model classifying each infection by means of a decision tree structured according to the probabilities of being symptomatic, severe or mild, and of showing different disease outcome including death, hospitalisation or ambulatory assistance (Fig. 4).
All learning curves confirm the fact that the soft decision tree structure is able to provide better generalization in contrast to the canonical hard decision tree structure.
