precede
发表于 2025-3-26 21:53:11
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Commemorate
发表于 2025-3-27 02:06:35
https://doi.org/10.1007/978-3-658-21300-8one-out stability by using Markov’s inequality, and then this bound is used to estimate the generalization error of classification learning algorithm. We compare the result in this paper with previous results in the end.
百灵鸟
发表于 2025-3-27 07:46:12
https://doi.org/10.1007/978-3-658-40783-4or classification of hatching eggs. Trained and tested by a great deal of samples, a reasonable neural network model is obtained. Its performance is measured in terms of two parameters: short computing time and accuracy in the classification process.
动脉
发表于 2025-3-27 11:25:01
https://doi.org/10.1007/978-0-230-37048-7 that this new network can recall the memorized patterns even with only a small fraction of total connections and is more sufficient than other networks with sparse topologies, such as randomly connected network and regularly network.
Exploit
发表于 2025-3-27 13:46:59
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音乐学者
发表于 2025-3-27 20:27:47
Commercial Vehicle Technology 2020/2021easured spectroscopic data. The true spectrum and the instrument response function are estimated simultaneously. In the preprocessing stage, the noise can be reduced in some degree. Experiments on some real measured spectroscopic data demonstrate the feasibility of this method.
affluent
发表于 2025-3-27 22:35:35
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尊敬
发表于 2025-3-28 02:34:49
Thomas Tentrup,Martin Wagner,Simon Strohcancer data set. Simulation results show that the obtained hierarchical B-spline network model has a fewer number of variables with reduced number of input features and with the high detection accuracy.
Allege
发表于 2025-3-28 09:41:32
Karsten Berns,Klaus Dressler,Martin Thulometrical method. We propose that the Gauss-Kronecker curvature of the statistical manifold is the natural measurement of the nonlinearity of the manifold. This approach provides a clear intuitive understanding of the model complexity.
遍及
发表于 2025-3-28 13:54:38
Commercial Vehicle Technology 2022 the self learning of penalty parameter and Kernel scale parameter in the support-vector-based procedures, which eliminates the need to search parameter spaces. Experiments on real datasets demonstrate performance and efficiency of PSCSV.