尤指植物
发表于 2025-3-21 17:33:51
书目名称Artificial Intelligence in Theory and Practice II影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162522<br><br> <br><br>书目名称Artificial Intelligence in Theory and Practice II读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162522<br><br> <br><br>
frenzy
发表于 2025-3-21 21:19:08
Enhancing RBF-DDA Algorithm’s Robustness: Neural Networks Applied to Prediction of Fault-Prone Softwnd (ii) to compare RBF-eDDA and MLP neural networks in software defects prediction. The simulations reported in this paper show that RBF-eDDA is able to correctly handle inconsistent patterns and that it obtains results comparable to those of MLP in the NASA data sets.
联想
发表于 2025-3-22 00:38:27
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allergy
发表于 2025-3-22 06:11:36
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漫步
发表于 2025-3-22 10:22:48
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凝视
发表于 2025-3-22 15:45:55
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Lineage
发表于 2025-3-22 20:45:12
Conference proceedings 2008formed part of the 20th World Computer Congress of IFIP, the International Federation for Information Processing (WCC-2008), in Milan, Italy in September 2008. The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (
commodity
发表于 2025-3-22 22:53:22
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JOG
发表于 2025-3-23 02:29:15
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aplomb
发表于 2025-3-23 08:17:51
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning Systemggest that altering the class distribution can improve the classification performance of classifiers considering AUC as a performance metric. Furthermore, as a general recommendation, random over-sampling to balance distribution is a good starting point in order to deal with class imbalance.