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The link between Normative Beliefs and Subjective Norm was also weak (weight of 0.10).
A bimodal distribution is only produced by rules with a very weak weight dependence (i.e. μ ≪ 1).
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Matrix contains the distances between the components of weak weights.
(M1) matrix contains the values of Extended Mahalanobis distances between the components of weak weights.
This allows the identification of the components of weak weights, the components of medium weights, and the components of strong weights.
So correctly classified (reliable) training samples are associated to strong weights and misclassified samples are assigned with weak weights in the reweighting process.
This distance is applied between the following components: Gaussian interblocks (reference/Current), the components of strong weights, the components of medium weights, and the components of weak weights (Figure 3). Figure 3 Extended Mahalanobis distance between the components of strong weights, the components of medium weights, and the components of weak weights.
For these types of Foreman sequence, about 80% minimum distances are in the matrix (distances between the components of weak weights).
After a number of iterations, strong weights will be assigned to the reliable samples and weak weights to the others, producing an optimized pixel-level training set (in form of optimized sample weights).
When modeling by a mixture of two Gaussian distributions, the cost function is defined by the Extended Mahalanobis distance between the components of strong weights and the components of weak weights.
A stronger error correction mechanism results in smaller steady-state weak weights.
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