The Bayesian regularization algorithm, also known as the Bayesian regularization method, is a powerful statistical technique used in machine learning and data analysis. It combines the principles of Bayesian statistics with regularization techniques to improve the accuracy and generalization of models. By incorporating prior knowledge and probabilities into the model, it helps to prevent overfitting and improves the model"s ability to handle complex data. This algorithm has been widely used in various fields, such as image recognition, natural language processing, and financial modeling. With its ability to handle uncertain and incomplete information, the Bayesian regularization algorithm has become an essential tool for building robust and reliable predictive models. Overall, it offers a versatile and effective approach to handling complex data analysis tasks.
贝叶斯正则化算法,Bayesian regularization algorithm
1)Bayesian regularization algorithm贝叶斯正则化算法
1.The Levenberg-Marquart(L-M)Bayesian regularization algorithm is combined with the back-propagation(BP) neural network to make the BP network achieve better generalization,faster speed of convergence and higher learning accuracy.将BP神经网络和L-M贝叶斯正则化算法相结合,可使BP神经网络在推广能力、收敛速度和逼近精度上能够获得很大的提高。
2)LM Bayesian regularization algorithmLM贝叶斯正则化算法
3)Bayesian-Regularization Algorithm贝叶斯正则化(BayesianRegularization)算法
4)Bayesian regularization贝叶斯正则化
1.The predictive models usingBayesian regularization neural network and grey model are established for the H+ concentration in the HPO, two kinds of model are compared and associated to establish model and to predict.以某己内酰胺厂磷酸羟胺(HPO)的制备的现场数据为基础,利用贝叶斯正则化神经网络和灰色模型建立了磷酸羟胺中的H+浓度的预测模型;比较了神经网络和灰色模型的差异,并把两者结合起来,建立模型进行预测。
2.This paper compares the results by standard BP algorithm with that ofBayesian regularization together with LM algorithm.比较了标准的BP算法和用贝叶斯正则化与Levenberg-Marquardt算法相结合的改进BP网络训练的结果。
3.The BPNN model ofBayesian regularization method 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。
英文短句/例句
1.Application of BP Neural Networks Based on Bayesian Regularization to Aircraft Wing Loads Analysis贝叶斯正则化BP网络在机翼载荷分析中的应用
2.Bayesian Regularization BP Neutral Network for the Construction of DEM Trend基于贝叶斯正则化BP神经网络的DEM趋势面逼近
3.Stock Index Forecast Based on Bayesian Regularization BP Neural Network基于贝叶斯正则化BP神经网络的股票指数预测
4.Research of Prediction of Coal and Gas Outburst Based on BP Artificial Neural Network Utilizing Bayesian Regularization基于贝叶斯正则化BP人工神经网络的煤与瓦斯突出预测的研究
5.A Model Based on Bayesian Regularization BP Neural Network to Predict the Probability of the Listed Companies Financial Crisis;基于贝叶斯正则化BP神经网络的上市公司财务困境预警模型
6.Agility evaluation of virtual enterprise based on BR neural network;基于贝叶斯正则化神经网络虚拟企业敏捷性评价
7.An Application of the TDBPNN Model Based on Bayes Regularization to Forecasting China s Foreign Trade and Evaluation;基于贝叶斯正则化的TDBPNN模型在中国外贸预报中的应用及评估
8.Bayesian Regularization Neural Network Ensemble Model Based on Partial Least Squares Regression and Its Application in Stock Market;偏最小二乘回归的贝叶斯正则化神经网络集成模型在证券分析预测中的应用
9.Regularized Kernel Density Estimation Algorithm Based on Sparse Bayesian Regression基于稀疏贝叶斯回归的正则化核密度估计算法
10.Bayesian-regularization BP Neural Network and the Application in Medical Fields;贝叶斯正规化BP神经网络及其在医学中的应用
11.admissibility of Bayes decision rule贝叶斯决策规则的宜取性
12.Bayse Principle Used in Land Resources Evaluation;贝叶斯准则在土地资源评价中的应用
13.Study the Bayesian-regularization BP Neural Network Model of Bacillary Dysentery基于贝叶斯正规化BP神经网络模型对菌痢发病的预测研究
14.Track Initiation Algorithm in Clutter Environments Based on High Order Correlations and Bayesian Rule基于高阶相关和贝叶斯规则的航迹起始算法
15.A Modified Bayes Classifier Using Association Rule Forest一种应用关联规则森林的改进贝叶斯分类算法
16.Text Topic Number Evaluation Based on Bayes Information Criteria基于贝叶斯信息准则的文本主题数估计
17.A Consistency Axiomatization of Correlatedly Bayesian Equilibrium相关贝叶斯平衡的一致性公理化(英文)
18.Spam Filtering Optimization Algorithm Based on Bayes;基于贝叶斯的垃圾邮件过滤优化算法
相关短句/例句
LM Bayesian regularization algorithmLM贝叶斯正则化算法
3)Bayesian-Regularization Algorithm贝叶斯正则化(BayesianRegularization)算法
4)Bayesian regularization贝叶斯正则化
1.The predictive models usingBayesian regularization neural network and grey model are established for the H+ concentration in the HPO, two kinds of model are compared and associated to establish model and to predict.以某己内酰胺厂磷酸羟胺(HPO)的制备的现场数据为基础,利用贝叶斯正则化神经网络和灰色模型建立了磷酸羟胺中的H+浓度的预测模型;比较了神经网络和灰色模型的差异,并把两者结合起来,建立模型进行预测。
2.This paper compares the results by standard BP algorithm with that ofBayesian regularization together with LM algorithm.比较了标准的BP算法和用贝叶斯正则化与Levenberg-Marquardt算法相结合的改进BP网络训练的结果。
3.The BPNN model ofBayesian regularization method 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。
5)Bayes regularization贝叶斯正则化
1.TheBayes regularization method is employed to get a well generalized neural networks, the redundant and irrelevant attributes can be deleted from the prime attributes set by pruning the input node of the networks.采用贝叶斯正则化方法训练 ,以得到推广性优良的神经网络 ,并提出启发性的遗传算法。
2.This paper establishes dynamic forward feedback correction model with the method of combiningBayes regularization and BP neural network.文中采用贝叶斯正则化与BP网络结合的方法,建立动态前馈校正模型。
3.Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizingBayes regularization.基于相空间重构的非线性预报思想,建立一个时滞的BP神经网络模型,采用贝叶斯正则化方法提高BP网络的泛化能力。
6)bayesian calibration贝叶斯校正算法
延伸阅读
正则化正则化regularization正则化fr卿Ila蛇口d皿;pel，”p“3叫。，」对于不适定问题(ili一posed problen招)构造关于初始数据的小扰动是稳定的近似解法(亦见正则化方法(爬洲a血王如n此thod)). B.只.Al，:eHHH，A.H.T拟“幻日撰【补注】“正则化”概念在数学中是相当一般的，它的含意已远远超过了处理不适定问题的正则化方法本身.它至少包含着下面两个交织在一起的思想.l)数学对象A由一个更正则的对象A雌作系统代换，一般要使得(A雌)咤=A雌.2)定义函数的值或对象的其他概念，而该值或概念是事先未予定义的(或者为无穷大，不确定，……).为此经常用一个合适的族(一个形变(由角亡几吐1011))，将上述对象置于族中，对该族中一切接近上述对象的对象定义函数值或者概念，然后取合适的极限.另一个技巧是除去“系统无穷大”，所使用的各种正则化方法的细节主要取决于特定的情况.也经常使用其他术语代替“正则化”，用来描述这种方法和技巧，譬如“正规化”，“重正规化”，“非奇异化”，“奇异性分解”，上面l)或2)(或两者)意义下的正则化例子有:正则化序列(参见序列的正则化(记孚面血以沁nofs叫uena治))，正则化算子和正则化解法(见不适定问题(沮一卯s司problen招);正则化方法(碳刻ari左山n服山团);积分方程，数值方法(i血脚l叫Ua石ons，nunrncalrr犯比Lods);F理dI幻Im方程，数值方法(Fre小hohn eq珑川on，nu“犯riaUn坦th以七))，最优化理论中的罚函数和其他正则化技术(见数学规划(灯以山既圈石司prog艺rnm止侣);罚函数法(体蒯tyftmctions，n犯让lodof))，各种重正规化方案(见重正规化(代泊。几阳面必石。n))，正规化和非奇异化格式及其变形(见正规概形(nonT以1 sche-此);奇点的分解(心olution of 51爬刘面胎))，分布的正则化(见广义函数(罗理几如沮几nctlon))，Stunn·Liou-访Ue算子的正规化的迹(见S如团.U俐币血问题(StunnLiouville prob」em))，和H沮笼成一S如耐血算子(H丑比找-Sc垃相dt opera加r)的正则化特征行列式.还有一个例子是用来定义泛函积分和量子场论中的某些无穷行列式(或其商)的乙函数正则化(欢扭-丘山以ion玛卿」面巨tion).其作法如下:令A是一个合适的算子，例如加plaCe或加place一氏1枉2而算子，定义它的广义C函数(罗】le爪血时欢扭一几nction) 心，(s)=艺*二’，其中又。在A的谱上取值(计算重数).至少在形式上有C’(、)l、一。二一艺。fog(*。)，这就提供了一个机会，由下式来定义C函数正则化行列式(欢饭一允扭无。n娜刘肚访习de加1和inant)det(A)二exP(一心’(s))}:二。
