珍珠无
发表于 2025-3-21 17:18:28
书目名称Machine Learning: ECML-95影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620758<br><br> <br><br>书目名称Machine Learning: ECML-95读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620758<br><br> <br><br>
吹牛者
发表于 2025-3-21 20:31:40
Multiple-Knowledge Representations in concept learning,owledge level to build an . correct . description of it. This approach is illustrated through our GEM system which learns concepts in a numerical attribute space using a Neural Network representation as the deep knowledge level and symbolic rules as the shallow level.
ear-canal
发表于 2025-3-22 00:45:54
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Enliven
发表于 2025-3-22 05:55:58
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NADIR
发表于 2025-3-22 09:39:32
Learning abstract planning cases,troduced model. An empirical study in the domain of process planning in mechanical engineering shows significant improvements in planning efficiency through learning abstract cases while an explanation-based learning method only causes a very slight improvement.
讨好美人
发表于 2025-3-22 15:42:20
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演绎
发表于 2025-3-22 19:25:07
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讨好美人
发表于 2025-3-23 01:12:18
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心胸狭窄
发表于 2025-3-23 03:48:16
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euphoria
发表于 2025-3-23 07:10:29
Analogical logic program synthesis from examples,d, two programs are said to be similar if they share a common explanation structure at an abstract level. Using this notion of similarity, we formalize an analogical logic program synthesis and show that our algorithm based on a framework of model inference can identify a desired program.