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In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning.
There exist several algorithms for unsupervised learning that are based on probabilistic reasoning, such as Bayes networks, graphical models, multiple eigenspaces, and different variants of HMMs.
For instance, context recognition methods based on probabilistic reasoning inherently suffer from ad-hoc static models, scalability, and data scarcity, and the ontology approach allows easy incorporation of machine understandability and domain knowledge, which provide rich expressiveness and facilitates reusability and intelligent processing at a higher level of automation [42].
In the next section, we discuss a second, more recent justification that is based on probabilistic reasoning.
Use of the InterVA method ensured that causes of death were standardized between study settings and over time (a significant advantage in the current study) and classification of asphyxia cases based on probabilistic reasoning relating to multiple causes may overcome misclassification bias associated with lay-reports and alternative methods of verbal autopsy interpretation.
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The first one is based on probabilistic estimation [9].
Will we toss the embryo, "start all over again and try for a better one?" Or change the offending genes based on probabilistic outcomes?
Bayesian approach is based on probabilistic learning.
Specifically, he said the available data tends to be less reliable because it's based on "probabilistic modeling".
Contemporary approaches based on probabilistic argumentation systems and probabilistic networks are better capable of handling these computational challenges.
These schemes are all based on probabilistic shared keys.
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based on probabilistic modeling
based on probabilistic linkage
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