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- W2061832015 abstract "As we reported previously, a deterministic hard-limited perceptron is a novel learning system in which the learning mechanism is different from most of the conventional learning systems. It is a noniterative, one-step learning system and yet is achieves most of the goals of the conventional learning. If an optimum algorithm is adopted in the design of this learning system, the learning is very fast and the recognition is very robust. This article reports the experimental results of several computer-implemented schemes of this novel learning system. It is seen that the system takes only one to two minutes to train several given patterns and the recognition of the untrained patterns is more than 80% successful. In one scheme, the recognition rate is almost 100% and the recognition is independent of the pattern size, pattern orientation, and pattern location.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only." @default.
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- W2061832015 date "1993-08-19" @default.
- W2061832015 modified "2023-09-26" @default.
- W2061832015 title "<title>Recognizing the untrained patterns in a noniterative perceptron learning scheme</title>" @default.
- W2061832015 doi "https://doi.org/10.1117/12.152621" @default.
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