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It is a probabilistic generative model and can be formed by RBMs as shown in Figure 3.
On the other hand, we construct a weak classifier for each local patch by logistic regression to fit our classification framework, since it is a probabilistic linear classifier.
Actually, G 2 : = G 1 ˜ and from Proposition 3.3 if f is a probabilistic G 1 -contraction, then it is a probabilistic G 2 contraction.
In actuality, it is a probabilistic process in which some traits make it more likely but do not guarantee that organisms possessing them will successfully reproduce.
Given that it is a probabilistic representation of the current conditions of a site or region, it cannot ensure the occurrence of landslides at a particular location, but only its occurrence probability.
Since the mapping f is a Banach G-contraction, then it is a probabilistic G-contraction (see Example 3.2) and property invokes that G is a ( C f ) -graph.
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It's a probabilistic statement, not a scientific one.
(Well, maybe not. It's a probabilistic result, after all).
Cons: It's a probabilistic approach that carries the risk of generating duplicates.
Meanwhile, we construct the weak classifier for each local patch by logistic regression to fit our classifying framework, due to it being a probabilistic linear classifier.
It is a new probabilistic logic method based on port redundancy and complementary data, oriented toward emerging technologies beyond CMOS, where the thermal noise could be predominant and the reliability of the future circuits could be limited.
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