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1.
分组样本下Bayes网络条件概率的学习算法   总被引:6,自引:0,他引:6  
本文应用带盘的Bayes网络作为分析模型,对于学习实例数据库为分组样本的关于各组样本私有条件概率的学习逄法进行讨论,构建出两层学习结构:第一层针对各组私有条件概率分布Θij(l)s的学习;第二层针对是各组公有条件概率分布Θij的学习,算法在综合公有后验条件概率分布和本组学习实例数据的基础上,实现对各组私有条件概率分布的学习,并可以通过调整组间差异性信度β值来改变综合值中共性和个性的比例。  相似文献
2.
贝叶斯学习中基于贝叶斯判别分析的先验分布选取   总被引:3,自引:0,他引:3       下载免费PDF全文
In this paper we propose an experimental method to choose a prior distribution. Different from many re-searchers, who offered lots of principles that separated from sample information, we consider it a Bayesian discrimina-tion problem combining with the sample information. We introduce the concept of Posterior belief about prior distri-butions. With the well-known Bayes theorem we give out a formula to calculate it and propose a method to discrimi-nate a prior between prior distributions-Highest Posterior Belief (HPB). We also show that under certain condition,the HPB method is identical with the ML-Ⅱ method.  相似文献
3.
SAR图像MAP降噪的精细研究   总被引:1,自引:0,他引:1  
本文推导出基于最大后验概率(MAP)滤波的一般形式,给出不同噪声分布和真实图像先验分布条件下的MAP滤波方程.从滤波方程在特定区间上解的分布情况以及区域统计特性分类两方面分析了MAP降噪性能,由此给出了MAP滤波的阈值表达形式.最后给出合成孔径雷达(SAR)图像的MAP降噪试验以及噪声滤除能力的量化指标.为了消除噪声强度对试验结果的影响,全面反映MAP降噪性能,本文给出了降噪能力随噪声大小的动态变化关系.结果表明,真实图像的先验分布对MAP滤波性能有着直接的影响,不合理的先验分布假定会严重降低MAP滤波的降噪能力.  相似文献
4.
Bayes decision problems require subjective elicitation of inputs: beliefs and preferences. Elicitation methods are far from simple, and elicited quantities cannot perfectly represent the judgements of the Decision Maker. Several foundations suggest overlaying this problem using robust approaches. In these models, beliefs are modelled by a class of probability distributions, and preferences by a class of loss functions. Then we have a Pareto order. So, the solution concept is an efficient set. In this paper, we deal with the problem of existence of a solution. Moreover, we study the relations between Bayes actions and nondominated ones.  相似文献
5.
将状态转移先验分布和观测似然分布相结合,提出一种基于自适应退火参数优化混合建议分布的粒子滤波算法。根据当前最新的观测信息,以退火参数因子调控混合建议分布中状态转移先验分布与似然建议分布的混合率。在混合建议分布中结合自适应参数优化机制动态调整退火参数。仿真实验验证了该算法的有效性。  相似文献
6.
杨颖涛  王跃钢  邓卫强  徐洪涛 《控制与决策》2012,27(9):1393-1396,1401
针对贝叶斯网络后验概率需计算样本边际分布,计算代价大的问题,将共轭先验分布思想引入贝叶斯分类,提出了基于共轭先验分布的贝叶斯网络分类模型.针对非区间离散样本,提出一种自适应的样本离散方法,将小波包提取模拟电路故障特征离散化作为分类模型属性.仿真验证表明,模型分类效果较好,算法运行速度得以提高,也可应用于连续样本和多分类的情况,扩展了贝叶斯网络分类的应用范围.  相似文献
7.
提出了贝叶斯学习中先验分布选取的一个新技术。该技术将若干个可能的先验进行加权平均,形成一个以权重为参数的线性联合先验,并通过选取权重参数得到一个最合适先验的一个近似。证明了线性联合先验的似然与其组合参数的似然的等价性,并提出了用极大似然或矩估计的方法来确定权重参数的值,从而得到一个最合适的线性联合先验。提出的线性联合先验及确定方法,使得可以利用样本数据对已知先验进行校正,导出未被发现的更合理的先验,从而使贝叶斯学习更为有效。  相似文献
8.
The generalized Dirichlet distribution has been shown to be a more appropriate prior for naïve Bayesian classifiers, because it can release both the negative-correlation and the equal-confidence requirements of the Dirichlet distribution. The previous research did not take the impact of individual attributes on classification accuracy into account, and therefore assumed that all attributes follow the same generalized Dirichlet prior. In this study, the selective naïve Bayes mechanism is employed to choose and rank attributes, and two methods are then proposed to search for the best prior of each single attribute according to the attribute ranks. The experimental results on 18 data sets show that the best approach is to use selective naïve Bayes for filtering and ranking attributes when all of them have Dirichlet priors with Laplace's estimate. After the ranks of the chosen attributes are determined, individual setting is performed to search for the best noninformative generalized Dirichlet prior for each attribute. The selective naïve Bayes is also compared with two representative filters for the feature selection, and the experimental results show that it has the best performance.  相似文献
9.
提出了一种基于自适应正则最大后验概率估计(MAP)的计算机断层(CT)图像重建方法,该方法可以自适应地选择正则化参数,并充分利用每一次迭代的重建结果的信息,不断对其进行更新,通过多次迭代得到最终的重建图像。通过对头部模型的一个切片进行仿真实验,验证了该方法的可行性。用实际实验数据,与传统的方法、最大似然期望(ML-EM)算法和滤波反投影(FBP)算法相比较,表明该方法适用性较强,能较好地保持图像的边缘信息,而且图像的信噪比较高。  相似文献
10.
Two new approaches to estimate Bayes factors in a finite mixture model context are proposed. Specifically, two algorithms to estimate them and their errors are derived by decomposing the resulting marginal densities. Then, through Bayes factor comparisons, the appropriate number of components for the mixture model is obtained. The approaches are based on simple theory (Monte Carlo methods and cluster sampling), what makes them appealing tools in this context. The performance of both algorithms is studied for different situations and the procedures are illustrated with some previously published data sets.  相似文献
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