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| LSTM× | Entropia próbkowa× | |
|---|---|---|
| Dziedzina≠ | Uczenie głębokie | Systemy złożone |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1997 | 2000 |
| Twórca≠ | Hochreiter, S. & Schmidhuber, J. | Richman & Moorman |
| Typ≠ | Recurrent neural network (gated memory cell) | Nonlinear entropy measure |
| Źródło pierwotne≠ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049. DOI ↗ |
| Inne nazwy | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells | SampEn, Sample Entropy (SampEn), Örneklem Entropisi, Nonlinear Complexity Measure |
| Pokrewne≠ | 5 | 2 |
| Podsumowanie≠ | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. | Sample Entropy (SampEn) is a nonlinear measure of the complexity and regularity of a time series. Introduced by Richman and Moorman in 2000 as an improvement over Approximate Entropy (ApEn), it quantifies the likelihood that similar patterns of a given length in the series remain similar when extended by one additional data point. A higher SampEn value indicates greater irregularity and complexity, while a lower value indicates more regularity or self-similarity. |
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