发现
发表于 2025-3-23 11:32:09
Simon Sohrt,André Heinke,Nikita Shchekutin,Björn Eilert,Ludger Overmeyer for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main clai
Glaci冰
发表于 2025-3-23 15:22:15
http://reply.papertrans.cn/43/4229/422851/422851_12.png
DALLY
发表于 2025-3-23 20:20:05
http://reply.papertrans.cn/43/4229/422851/422851_13.png
同步信息
发表于 2025-3-23 22:34:53
http://reply.papertrans.cn/43/4229/422851/422851_14.png
chlorosis
发表于 2025-3-24 02:37:38
http://reply.papertrans.cn/43/4229/422851/422851_15.png
不出名
发表于 2025-3-24 07:33:11
http://reply.papertrans.cn/43/4229/422851/422851_16.png
linear
发表于 2025-3-24 12:48:22
http://reply.papertrans.cn/43/4229/422851/422851_17.png
Bravado
发表于 2025-3-24 17:16:21
Christopher Kirsch,Sören Kerner,Alexander Bubeck,Matthias Gruhlerhes.Presents alternative probabilistic search algorithms thaThis book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of suc
Abnormal
发表于 2025-3-24 21:01:29
http://reply.papertrans.cn/43/4229/422851/422851_19.png
epidermis
发表于 2025-3-24 23:53:05
hes.Presents alternative probabilistic search algorithms thaThis book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of suc