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The combination of the decision region of RDP models, by using boolean operations, is also discussed.
The decision region is shown in Figure 4b.
where is the decision region for the code index pair.
Here, threshold can be fixed based on the optimal decision region.
It may be H1 or H0 depending upon the decision region.
Note that some boundaries lie inside the decision region corresponding to one hierarchical codeword (given by the same region colour).
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You have decision regions defined by the attractor states and the boundaries between those.
Counter-intuitively, in certain decision regions, sharing demand information can do more harm than good.
We also provide an intuitive geometric interpretation for the partitioning of the observations into decision regions.
Other existing supervised learning methods may have the restriction that only allows decision regions to be convex.
We show that these algorithms behave like sample selectors: samples near the decision regions are the most relevant during learning.
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