Method for adapting a similarity function for identifying misclassified software objects
作者： Robert W. Schwanke
DOI:
关键词: Current (mathematics) 、 Feature (computer vision) 、 Software 、 Function (mathematics) 、 Similarity (geometry) 、 Resolution (algebra) 、 Computer science 、 Algorithm 、 Group (mathematics) 、 Set (abstract data type) 、 Artificial intelligence
摘要: A method of reoptimizing the coefficients a similarity function coefficient estimation as mavericks are resolved in maverick analysis comprises computing initial weights for each feature and passing to an procedure, along with software objects, their group assignments, peer parameter K confidence N. Receiving output using updated values obtain lists misclassified poor-confidence placing them Current Maverick Set. Presenting Set analyst determine (1) if should be deferred placed Deferred Set; or (2) is assigned certain it removed from Firmly Assigned (3) input set objects have features added to, them, (4) returned procedure wherein this time, its inputs are: original less members plus previously used, which may modified need be; assignments. Updated received, when resolution complete, stops.
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