2016
DOI: 10.1007/978-3-319-30695-7_26
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Augmenting the Algorithmic Structure of XCS by Means of Interpolation

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Cited by 11 publications
(6 citation statements)
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“…Also in the context of OC, Stein et al proposed to make use of interpolation techniques to generate new classifiers (Stein et al, 2016). The basic idea is that the best action is probably a compromise of the actions proposed by the best surrounding classifiers in the niche.…”
Section: State Of the Art In Active Reinforcement Learningmentioning
confidence: 99%
“…Also in the context of OC, Stein et al proposed to make use of interpolation techniques to generate new classifiers (Stein et al, 2016). The basic idea is that the best action is probably a compromise of the actions proposed by the best surrounding classifiers in the niche.…”
Section: State Of the Art In Active Reinforcement Learningmentioning
confidence: 99%
“…This work draws on the methods proposed in [20][21][22][23][24]. The authors used interpolation in combination with an XCS classifier System to speed up learning in single-step problems by using previous experiences as sampling points for interpolation.…”
Section: Related Workmentioning
confidence: 99%
“…To fill existing knowledge gaps, as formerly defined by Stein et al (Stein et al, 2018), active learning (Cohn et al, 1994) can be employed (Stein et al, 2017a). In addition we will expect to be able to use interpolation between known classifiers to gain knowledge on inter-laying classifiers (Stein et al, 2016;Stein et al, 2017b). Figure 4 illustrates the basic procedure.…”
Section: Learning Quality Prediction and Strategy Selectionmentioning
confidence: 99%