Planning with ensembles of classifiers
Frontiers in Artificial Intelligence and Applications, ISSN: 1879-8314, Vol: 263, Page: 1007-1008
2014
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures6
- Readers6
Conference Paper Description
Learning search control for forward state planning has been previously addressed as a relational classification task, where predictions are used to generate action policies. In this paper, we describe a new bagging approach to learn and apply ensembles of relational decision trees to generate more robust policies for planning. Preliminary experimental results demonstrate that new policies produce on average plans of better quality.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84923162998&origin=inward; http://dx.doi.org/10.3233/978-1-61499-419-0-1007; https://www.medra.org/servlet/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=263&spage=1007; https://dx.doi.org/10.3233/978-1-61499-419-0-1007; https://ebooks.iospress.nl/publication/37093
IOS Press
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