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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Introduction to Extreme Seeking Entropy.

Jan Vrba1, Jan Mareš1,2

  • 1Department of Computing and Control Engineering, Faculty of Chemical Engineering, University of Chemistry and Technology, 166 28 Prague, Czech Republic.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a novel measure for adaptive system learning effort to detect data novelties. The generalized Pareto distribution is used for improved novelty detection in various scenarios.

Keywords:
extreme seeking entropylearninglearning entropylearning systemnovelty detectiontime series

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Area of Science:

  • Machine Learning
  • Data Science
  • Signal Processing

Background:

  • Evaluating adaptive system learning effort is crucial for novelty detection.
  • Existing methods may not fully capture the nuances of learning dynamics.

Purpose of the Study:

  • Introduce a new, adaptable measure for quantifying learning effort in adaptive systems.
  • Develop a robust method for detecting novelties using this learning effort measure.

Main Methods:

  • Employed the generalized Pareto distribution for estimating unusual update probabilities.
  • Utilized adaptable parameters within the proposed learning effort measure.
  • Tested the method across synthetic and real-world time series datasets.

Main Results:

  • The proposed learning effort measure effectively detected novelties in diverse experimental settings.
  • Successful validation was achieved with multiple adaptive filters and learning algorithms.
  • Demonstrated the efficacy of the generalized Pareto distribution in novelty detection.

Conclusions:

  • The new learning effort measure provides a powerful tool for novelty detection in adaptive systems.
  • The generalized Pareto distribution offers a statistically sound approach for identifying unusual system updates.
  • The method's versatility across different datasets and algorithms highlights its practical applicability.