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Human experts constitute pattern classes of natural objects based on their observed appearance.
Selecting suitable features for pattern classes is a critical process in the OMR system.
Typically, Two pass approach coarsely classifies an unknown sample pattern in a group of pattern classes in first-pass.
In addition, OVON is robust to sensor failures and OCON is well suited for addition of new pattern classes.
This highlights deficiencies in the reviewed patterns and provides a basis for identifying new or "missing" patterns and pattern classes.
Despite this restriction, super-linear capacity in Hopfield networks is possible for special pattern classes and connectivity structures.
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A pattern class is a collection of patterns that share some common features and characteristics.
Each different pattern class corresponds to the hand in a different pose, or set of poses.
The training error for a single pattern (class 0) can be seen in Figure 12.
The potentials can be used to measure the density of different regions in the pattern class.
Based on a proper sample calculation, three groups of 15 CBCT images each were made according to their ANB angle and facial pattern: class I (normo facial pattern) and class II and III (long facial pattern).
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