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- W2809945604 abstract "Adaptive systems typically comprise components that interrelate and interact to enable the whole system to respond and adjust to changes in the environment, operator, and task in order to regulate or maintain a level of performance or homeostasis. In so doing, they enable a degree of individualization and customization for many technological innovations such as managing the use of automation. Adaptive systems often involve some kind of feedback or closed-loop which requires a criteria for determining invoking thresholds, as well as some type of classification algorithm that models the type of changes to which the system has to adapt. This paper outlines the issues, considerations, and challenges associated with classification in adaptive systems, and reviews several algorithms that implement the feedback loop in neuro-ergonomic applications. These include logistic regression, Naive-Bayes, artificial neural networks (ANN), and support vector machines (SVM) techniques." @default.
- W2809945604 created "2018-07-10" @default.
- W2809945604 creator A5064078390 @default.
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- W2809945604 date "2018-06-27" @default.
- W2809945604 modified "2023-09-24" @default.
- W2809945604 title "Classification Algorithms in Adaptive Systems for Neuro-Ergonomic Applications" @default.
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- W2809945604 doi "https://doi.org/10.1007/978-3-319-94223-0_39" @default.
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