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Titlebook: Learning with Nested Generalized Exemplars; Steven L. Salzberg Book 1990 Kluwer Academic Publishers 1990 algorithms.artificial intelligenc

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21#
發(fā)表于 2025-3-25 06:27:36 | 只看該作者
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發(fā)表于 2025-3-25 09:02:06 | 只看該作者
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發(fā)表于 2025-3-25 13:05:29 | 只看該作者
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發(fā)表于 2025-3-25 23:04:16 | 只看該作者
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發(fā)表于 2025-3-26 04:00:12 | 只看該作者
Experimental results with NGE,roblems: (1) predicting the recurrence of breast cancer, (2) classifying iris flowers, (3) predicting survival for heart attack victims, and (4) predicting the dependent variable in a discrete event simulation. The simulation problem was based on a very simplified stock market model. In the process
27#
發(fā)表于 2025-3-26 05:59:46 | 只看該作者
Conclusion,verse set of domains. The . system displayed robustness in the face of both noise and incomplete data. Comparisons with experts’ performance, where such comparisons were possible, were quite favorable, and in one domain — breast cancer prediction — the program performed significantly better than the
28#
發(fā)表于 2025-3-26 09:27:00 | 只看該作者
29#
發(fā)表于 2025-3-26 14:14:10 | 只看該作者
Learning with Nested Generalized Exemplars978-1-4613-1549-0Series ISSN 0893-3405
30#
發(fā)表于 2025-3-26 19:24:48 | 只看該作者
Conclusion,verse set of domains. The . system displayed robustness in the face of both noise and incomplete data. Comparisons with experts’ performance, where such comparisons were possible, were quite favorable, and in one domain — breast cancer prediction — the program performed significantly better than the experts.
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