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137 375 (0,183s)

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Batch Algorithm of Learning Entropy in Software Analysis for Test Data

The use of the Learning Entropy (LE) algorithm to detect anomalies in component testing was proposed in the original software developed for the analysis of component test data for the automotive industry. The article summarizes the ...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2020
  • D
  • Link
Result

Learning Entropy: Multiscale Measure for Incremental Learning

., Entropy of Learning, or in short the Learning Entropy. This novel cognitive measure can be used for evaluation of each newly measured sample of data, or even of whole intervals. The Learning

BC - Teorie a systémy řízení

  • 2013
  • Jx
  • Link
Result

TSEntropies

Implementation of original and modified time series entropy algorithms.

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2019
  • R
  • Link
Result

AISLEX: Approximate individual sample learning entropy with JAX

We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. AISLEX detec...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2024
  • Jimp
  • Link
Result

Acceleration of time series entropy algorithms

This paper concentrates on the entropy estimation of time series. Two new algorithms are introduced: Fast Approximate Entropy and Fast Sample Entropy. Their main advantage is their lower time complexity. E...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2019
  • Jimp
  • Link
Result

Learning Entropy of Adaptive Filters via Clustering Techniques

Learning Entropy (LE) was initially introduced as a measure for sample point novelty by unusually large learning effort of an online learning system. The key and the learning state space so that L...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2020
  • D
  • Link
Result

Learning Entropy: On Shannon vs. Machine-Learning-Based Information in Time Series

Individual Sample-point Learning Entropy (AISLE). For data series, then, the LE shouldThe paper discusses the Learning-based information (L) and Learning Entropy (LE) in contrast to classical Sha...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2022
  • D
  • Link
Result

Correlation minimizing replay memory in temporal-difference reinforcement learning

Online reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approximation, thus the le...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2020
  • Jimp
  • Link
Result

Study of Learning Entropy for onset detection of epileptic seizures in EEG time series

This paper presents a case study of non-Shannon entropy, i.e. Learning Entropy samples of data to the contemporary learned governing law of a learning system. For comparison, the principle and the...

BC - Teorie a systémy řízení

  • 2016
  • D
  • Link
Result

New Fast ApEn and SampEn Entropy Algorithms Implementation and Their Application to Supercomputer Power Consumption

Approximate Entropy and especially Sample Entropy are recently frequently used, namely Fast Approximate Entropy and Fast Sample Entropy. All these algorithms important result is ...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2020
  • Jimp
  • Link
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