书法
发表于 2025-3-23 10:25:30
Yvonne Staack Diplompädagogin,Wolfgang Wittwerlearning, we obtain a semantic channel from a sampling distribution or Shannon’s channel. If samples are huge, we can directly convert a Shannon’s channel into a semantic channel by the third kind of Bayes’ theorem; otherwise, we can optimize the membership functions by a generalized Kullback–Leible
dry-eye
发表于 2025-3-23 17:05:08
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碎石头
发表于 2025-3-23 21:35:30
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最高峰
发表于 2025-3-24 00:04:24
Andreas Diettrichchnology. Here is an example of artificial intelligence visual art research. The method is: First of all, from the characteristics of artistic language, explore the charm of human intelligence. Furthermore, from the characteristics of technical language, we can feel the power of artificial intellige
冰雹
发表于 2025-3-24 03:42:21
Uwe Elsholzlearning, we obtain a semantic channel from a sampling distribution or Shannon’s channel. If samples are huge, we can directly convert a Shannon’s channel into a semantic channel by the third kind of Bayes’ theorem; otherwise, we can optimize the membership functions by a generalized Kullback–Leible
时代
发表于 2025-3-24 08:19:39
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Little
发表于 2025-3-24 11:16:36
Franziska Kupfer M.A. Erwachsenenbildung und Diplom-Kulturpädagogin (FH) of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming.
cruise
发表于 2025-3-24 17:14:15
of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming.
推延
发表于 2025-3-24 20:30:41
Markus Walberon theory. This theory uses the P-T probability framework so that likelihood functions and truth functions (or membership functions), as well as sampling distributions, can be put into the semantic mutual information formula at the same time. Hence, we can connect statistics and (fuzzy) logic. Rate-
Pamphlet
发表于 2025-3-25 01:55:25
Lars Schlenkeron theory. This theory uses the P-T probability framework so that likelihood functions and truth functions (or membership functions), as well as sampling distributions, can be put into the semantic mutual information formula at the same time. Hence, we can connect statistics and (fuzzy) logic. Rate-