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A note on comparing classifiers
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DOI：10.1016/0167-8655(95)00113-1.png)
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Abstract
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Recently many new classifiers have been proposed, mainly based on neural network techniques. Comparisons are needed to evaluate the performance of the new methods. It is argued that a straightforward fair comparison demands automatic classifiers with no user interaction. As this conflicts with one of the main characteristics of neural networks, their flexibility, the question whether they are better or worse than traditional techniques might be undecidable.
Keywords:
automatic classifiers
benchmarking
comparisons
feedforward neural networks
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