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- W2911969372 abstract "Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is that the learning effort of learning systems is quantifiable as a novelty measure for each individually observed data point of otherwise complex dynamic systems, while the model accuracy is not a necessary requirement for novelty detection. This brief paper extends the explanation of LE from the point of an informatics approach towards a cognitive (learning-based) information measure emphasizing the distinction from Shannon’s concept of probabilistic information. Fundamental derivations of learning entropy and of its practical estimations are recalled and further extended. The potentials, limitations, and, thus, the current challenges of LE are discussed." @default.
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- W2911969372 date "2019-02-11" @default.
- W2911969372 modified "2023-09-24" @default.
- W2911969372 title "Learning Entropy as a Learning-Based Information Concept" @default.
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- W2911969372 doi "https://doi.org/10.3390/e21020166" @default.
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