1) regularization coefficient 正则项系数1. A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient. 提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法。
Learning Rate and Regularization Coefficient-Free Latent Factor Analysis via PSOdoi:10.1007/978-981-19-6703-0_3A big-data-related application commonly has numerous nodes, e.g., users and items in a recommender system [1–5]. With the exponential growth of involved nodes, it is impossible to...
The least-square regression problem is considered by coefficient-based regularization schemes with penalty. The learning algorithm is analyzed with samples drawn from unbounded sampling processes. The purpose of this paper is to present an elaborate concentration estimate for the algorithms by means of ...
Presented here is a new local adaptive grouped regularization (LAGR) method for local variable selection in spatially varying coefficient linear and generalized linear regression. LAGR selects the covariates that are associated with the response at any point in space, and simultaneously estimates the ...
This regularization, together with the use of B-spline functions, is shown to provide accurate numerical methods of crack analysis in 3D time harmonic ... NN Kobayashi - 《Computational Mechanics》 被引量: 75发表: 1989年 Splines are Universal Solutions of Linear Inverse Problems with Generalized-...
We study the backward problem with time-dependent coefficient which is a severely ill-posed problem. We regularize this problem by combining quasi-boundary value method and quasi-reversibility method and then obtain sharp error estimate between the exact
inverse problemparabolic equationtotal variationoptimal controlBased on the optimal control framework,a numerical method for solving the unknown coefficient of the two order parabolic equation is discussed by using the total variation regularization method.Combining with the direct problem a necessary ...
Code for "Sparse spatially clustered coefficient model via adaptive regularization" Zhong, Yan, Huiyan Sang, Scott J. Cook, and Paul M. Kellstedt. "Sparse spatially clustered coefficient model via adaptive regularization." Computational statistics & data analysis 177 (2023): 107581. https://doi....
Sensitivity of the (solid line) and correlation (dashed line) to the choice of the regularization coefficient.Jörn, DiedrichsenNikolaus, Kriegeskorte
神经网络模糊规则推理泛化能力正则化Based on bias variance model, a novel method of dynamically tuning the regularization coefficient by fuzzy rules inference was proposed. The fuzzy inference rules and membership functions were effectively determined. Furthermore, the method was compared with the ...