A column-based classifier as a method for pattern recognition and clustering built on the notion of inverse patterns is considered. This approach replaces the matching of unknown patterns to prototype patterns with the intersection operations for inverse images. These operations are much less computationally intensive than comparison operations. A possible analogy with hashing is that the features of the pattern being recognized are used as addresses that play the role of hash function arguments that define the name of the pattern class without search operations like in hashing. As an example of a practical implementation of the proposed approach the recognition problem for dynamically changing patterns represented by video clips is solved.
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