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The creation of general knowledge (i.e., abstraction, generalization, context reduction, theory elicitation) is a relevance-driven process done by "stripping away irrelevancies" [ 9].
Analyzing an image or video sequence in terms of patches, rather than individual pixels/descriptors, has some inherent advantages (i.e., computation, generalization, context, etc).
Those comparisons will be made in a generalization context: the faces used to construct the model (18 persons from the M2VTS database [49]) and the ones used for the tests come from distinct databases.
It is important to clarify that the use of absolute cutoff points is not feasible, since in a generalization context, it is not possible to suggest any arbitrary fixed number of terms to consider.
Consequently, the costs associated with mistakes are much higher in the non-predator generalization context and we could then predict a much narrower frame of generalization, if any, in this context.
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Participants subsequently learned to categorize novel background colors (ie, generalization contexts) as being similar to either the unpredictable or predictable pain context.
Results indicated higher pain-related fear reports, but no elevated startle responses, for generalization contexts that were trained to be similar to the original unpredictable context rather than the predictable pain context.
We can predict the costs associated with wrong associations in generalization contexts.
The article also examines the tradeoffs between large-N and limited-N comparisons in terms of generalization and context, and stresses the need for stronger theory the smaller the number of cases being sampled.
On the other hand, incorporating general and highly overlapped contextual factors overcomes the latter shortcoming and provides efficient context generalization, but its training procedure becomes more computationally complex.
The most significant drawback of HSMM, namely inadequate context generalization, is also pointed out.
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