里程表
发表于 2025-3-21 17:04:21
书目名称Inductive Logic Programming影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0463906<br><br> <br><br>书目名称Inductive Logic Programming读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0463906<br><br> <br><br>
Exterior
发表于 2025-3-21 23:49:33
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ITCH
发表于 2025-3-22 04:18:05
Efficient Cross-Validation in ILP,iscuss two issues: a) the existence of dependencies between parts of a query that limit the obtainable efficiency improvements and b) the combination of parallel cross-validation with query-packs. Tentative solutions are proposed and evaluated experimentally.
有其法作用
发表于 2025-3-22 07:50:26
A Genetic Algorithm for Propositionalization, individual, and general set-based operators to alter one partition or to mix two ones. For propositionalization, operators are extended to change not only the partition but also the associated numeric constraints.
沙草纸
发表于 2025-3-22 09:23:43
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宽容
发表于 2025-3-22 13:24:02
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Intellectual
发表于 2025-3-22 19:25:38
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使声音降低
发表于 2025-3-23 00:55:17
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Acetaminophen
发表于 2025-3-23 01:45:49
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lanugo
发表于 2025-3-23 07:47:54
Towards Combining Inductive Logic Programming with Bayesian Networks,ponents of Bayesian logic programs. More precisely, we show how to combine the ILP setting . with score-based techniques for learning Bayesian networks. Thus, the paper positively answers Koller and Pfeffer’s question, whether techniques from ILP could help to learn the logical component of first order probabilistic models.