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Once the saliency map is extracted (using the visual attention method), we apply a Linear Discriminant Analysis (LDA) [9] to estimate the probability of object presence.
Interestingly, different paths can have different factors that directly affect the probability of object identification, such as the number of SNs and the number of silhouettes stored in the database.
Similarly, different paths can have different factors that directly affect the probability of object identification, such as the number of SNs deployed in the RoI and number of silhouettes stored in database; therefore, when two MOs traverse the same path, the surety levels of these MOs can be different, depending upon different underlying factors for each MO.
The lower approximation identifies those objects that belong with certainty to a pattern; the upper approximation identifies those objects that have some probability of belonging to some pattern.
For the second case, we use machine learning techniques to estimate the probability of objects supporting each other.
The indicators of economic status also significantly impact the probability of objecting to pay for physician services.
Of course, the site still provides some probabilities of getting certain diseases.
The color-based method is used for the color probability distribution of object tracking.
Correlation degree is denoted as a local probability P of object existence, and is calculated as follows: P = e − d − d ref 2 d ref 2 = e − ∑ o = 1 N d o − d ro 2 ∑ o = 1 N d ro 2 (5).
LEC evaluates a Web object based on its cost per unit size multiplied by the estimated reference probability of the object.
High value of ER means that the probability of false object tracking is high.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com