There have been may attempts to identify an un- known input pattern from patterns having many categories represented by multiple-dimension feature vectors, but they require a very large computation time when a conventional method is applied. This article proposes a high-speed auto- matic method of determining a small number of likely categories without calculating distances to standard pat- terns. In the proposed method, the region of existence of samples on each feature axis is defined by using the learning- sample distribution, and the region is divided into l cells.
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