In a number of previous publications, we have stated, without proof, that the total fraction of samples discarded by the multiedit algorithm is bounded from above by 2 E 1 , where E 1 is the 1-NNR error rate for the i...
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In a number of previous publications, we have stated, without proof, that the total fraction of samples discarded by the multiedit algorithm is bounded from above by 2 E 1 , where E 1 is the 1-NNR error rate for the initial distributions. It is the purpose of this note to offer a more precise formulation together with a derivation of this assertion.
In this paper we address the elusive problem of selecting references (templates) for minimum distance classification when the number of pattern classes is very large. We argue that the multiedit/condensing technique o...
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In this paper we address the elusive problem of selecting references (templates) for minimum distance classification when the number of pattern classes is very large. We argue that the multiedit/condensing technique offers an automatic solution to this problem which avoids the proliferation of references without impairing the recognition performance. The effectiveness of the approach is demonstrated by experimental results in a print recognition context. Suggestions are made about ways of circumventing problems of computational complexity.
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