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An SVM can train with a large number of patterns.
But analyzing a large number of patterns is time-consuming for users, and many irrelevant patterns may be found.
Obviously, a large number of patterns can approximate the different shapes of the MRs well but require more bits to identify the pattern itself.
Second, these methods can generate a large number of patterns since they only consider the minimum support as constraint to select patterns.
Owing to the large number of patterns that do not belong to the MCs class, with respect to the number of patterns that do belong to the MCs class, balancing was performed.
The McFIS-PVC requires extra time to process a large number of patterns (i.e., 32 patterns), while the McFIS-ASPVC requires extra time to generate patterns from the video frames.
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However, CPM is computationally challenging, mainly due to the large number of pattern instances hidden in spatial data.
We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.
We were also able to identify a large number of patterning genes.
In the latter, a large number of pattern-associated molecular patterns will flood the local TLR cell surface array.
Acceptance of continuous and nominal variables, capacity to learn and recall large numbers of patterns, experienced-based learning, supervised learning, classification accuracy, user parameter and the ability to predict the training times are some of the design goals that could be noted for an AIRS-like supervised learning system.
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significant number of patterns
large number of configurations
large number of frames
huge number of patterns
large number of behaviors
large number of schemes
large number of paintings
large number of orientations
enormous number of patterns
large number of measurements
large number of plans
large number of change
large number of master
large number of frame
large number of variables
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