dlcp2023:abstracts
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| dlcp2023:abstracts [21/06/2023 00:20] – [6. Preliminary results of neural network models in HiSCORE experiment] admin | dlcp2023:abstracts [05/03/2025 17:33] (current) – ↷ Links adapted because of a move operation 156.59.198.135 | ||
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| ====== Book of Abstracts ====== | ====== Book of Abstracts ====== | ||
| - | //Draft June5, 2023 // | + | |
| + | {{ dlcp2023: | ||
| ===== Plenary reports ===== | ===== Plenary reports ===== | ||
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| //Poster// | //Poster// | ||
| - | __V.Kalninsky__ | + | __V.Kalnitsky__ |
| The problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We have proposed an approximation of the signum function by a smooth spline. We are controlling the difference between the soft connective and the associative connective. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks. | The problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We have proposed an approximation of the signum function by a smooth spline. We are controlling the difference between the soft connective and the associative connective. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks. | ||
dlcp2023/abstracts.1687296002.txt.gz · Last modified: 21/06/2023 00:20 by admin