Xin J H,Shao S,Chung K F.Colour-appearance modeling using feedforward networks with Bayesian regularization method-Part Ⅰ:Forward model. Color Research and Application . 2000Xin;J;H;Shao;S;Chung;K;F.Colour-appe
Optimisation of neural state variables estimators of two-mass drive system using the Bayesian regularization methodOptimisation of neural state variables estimators of two-mass drive system using the Bayesian regularization methodelectrical drivetwo-mass systemstate...
【ML】最小二乘Least squares,最大似然maximum Likelihood,贝叶斯bayesian method,正则项regularization mothed 两个重要观点: 最小二乘数学建模等价于高斯噪声最大释然估计统计建模 正则化最小二成等价于基于高斯噪声的最大化后验概率统计建模 几乎所有的机器学习方法也许建立之初没有什么统计解释,最后大家发现,都可以通过...
The Levenberg–Marquardt algorithm is incorporated into the back-propagation procedure to accelerate the training of FNNCAM and the Bayesian regularization method is applied to the training of neural networks to improve generalization. The results of FNNCAM obtained are quite promising. 2000 John Wiley...
The BPNN model ofBayesian regularizationmethod was adopted to create the adaptivity and generalization of BPNN. 基于混沌退火算法和BPNN模型的末敏弹系统效能参数优化,引入贝叶斯正则化方法的BPNN模型,使神经网络具有自适应性和推广能力。 更多例句>> 5) Bayes regularization ...
贝叶斯神经网络,简单来说可以理解为通过为神经网络的权重引入不确定性进行正则化(regularization),也相当于集成(ensemble)某权重分布上的无穷多组神经网络进行预测。 本文主要基于 Charles et al. 2015 [1]…
As such, the deterministic counterpart of the Bayesian inference scheme incorporates regularization in a natural way, without having to revert to re-parameterization or other standard regularization methods that require additional decision-making and may appear heuristic. Moreover, information contained in ...
(4) The maximal values of posterior distribution can be automatically obtained through Bayesian regularized method. This facilitates the selection of regularization parameters, possesses good robustness and excellent ?tting. So, it can be concluded that BRBPNNs must be extensively applied in ...
RegularizationPerson re-identification across disjoint cameras has attracted increasing interest in computer vision due to its wide potential applications in visual surveillance. In this paper, we propose a new regularized Bayesian metric learning (RBML) method for person re-identification. While numerous...
Method The construction of neural network is performed by using the single hidden layer and the optimization of Bayesian regularization. A dataset is assembled using the explicit Runge-Kutta technique for reducing the mean square error using the training 76 %, while 12 %, 12 % for validation and...