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So grid labeling outputs label proportions of each cell in the training data.
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The first (n−1 -RANC produces the outputs labeled 0 to 2n−1 -RANCd the second produces outhets 2n−1+1 toutputs
Somewhat obvious modifications are also required for the single n-RANCs that produce the outputs labeled 0 and 2n−1 so that they are produced according to the logic identities 3 and 4 in Table 2.
These n-RANCs are shown on the left and right sides of Fig. 1A D and the left of Fig. 2. The single n-RANCs for the outputs labeled 2n−1−1 and 2n−1 are constructed differently from the recursive definition of a BC n-RANC because they must conform to equations 1 and 2 in Table 2 to have the interval measure property.
This modular architecture makes assembly of the network considerably simpler than the result appears in Fig. 2. To facilitate clarification of subsequent diagrams, one of the excitatory connections is drawn in green in Fig. 4F, G. Non-hexagonal n-RANCs for the outputs labeled 2n−1−1 and 2n−1 are also modular, as described previously in the recursive process for constructing them.
With all inputs and outputs labeled, we obtain the CNF formula for each gates and the entire circuit.
The method of adjusting output label values is used to prevent the output saturation problem for multi-class classification.
A feature element that falls into an interval will be mapped to the corresponding 1-bit output label.
Another area of future work pertains to the scenario where the output label space is different between domains.
All training sentences used in building the voice are synthesized where phoneme durations are also generated in the form of output label files.
The variable θ, which parametrizes the relationship between the topic indicators and the output label, is estimated in an iterative manner along with the topics.
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