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Titlebook: Learning from Data; Artificial Intellige Doug Fisher,Hans-J. Lenz Book 1996 Springer-Verlag New York, Inc. 1996 Bayesian network.Likelihood

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樓主: T-cell
11#
發(fā)表于 2025-3-23 11:53:44 | 只看該作者
Liping Liuproduced by the effective medium is due to scattering noise arising from the layering microstructure. We model this fine scale layering as a rapidly varying stochastic process. This scattering noise component of MT data is fundamental since it arises from the very structure of the medium being probe
12#
發(fā)表于 2025-3-23 16:06:03 | 只看該作者
13#
發(fā)表于 2025-3-23 21:12:50 | 只看該作者
Prakash P. Shenoyproduced by the effective medium is due to scattering noise arising from the layering microstructure. We model this fine scale layering as a rapidly varying stochastic process. This scattering noise component of MT data is fundamental since it arises from the very structure of the medium being probe
14#
發(fā)表于 2025-3-24 00:02:36 | 只看該作者
Xiaorong Sun,Steve Y. Chiu,Louis Anthony Coxmicrostructured system. Also included are studies of homogenisation, that field which seeks to determine equivalent homogeneous systems which can give equivalent wave properties to structured materials, and inverse problems, in which waves are used as a probe to infer structural details concerning s
15#
發(fā)表于 2025-3-24 03:18:05 | 只看該作者
16#
發(fā)表于 2025-3-24 06:35:27 | 只看該作者
John F. Elder IVt the effect of electric fields on crack propagation in the FGPMs is qualitatively the same as that in a homogeneous piezoelectric material, i.e., the gradual variation of material does not change the propagation tendencies of cracks under an electric field.
17#
發(fā)表于 2025-3-24 13:41:40 | 只看該作者
J?rg Gebhardt,Rudolf Kruset the effect of electric fields on crack propagation in the FGPMs is qualitatively the same as that in a homogeneous piezoelectric material, i.e., the gradual variation of material does not change the propagation tendencies of cracks under an electric field.
18#
發(fā)表于 2025-3-24 16:43:30 | 只看該作者
19#
發(fā)表于 2025-3-24 20:16:51 | 只看該作者
Using Causal Knowledge to Learn More Useful Decision Rules From Dataion trees can be validated by methods such as cross-validation (Breiman et al., 1984), and they can easily be modified to handle missing data by constructing rules that exploit only the information contained in the observed variables.
20#
發(fā)表于 2025-3-25 00:21:48 | 只看該作者
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