加入领域先验知识的产生式情感分类模型

情感分类是通过分析数据中的情感信息,来预测数据所传递的情感倾向.其中结合语言学词典与产生式分类器构造带有先验知识的分类模型,是一类重要的研究课题.通过研究情感词的领域性和不同权重的特性,提出了一种新的融入情感先验知识的情感分类方法.通过自动分析构造领域相关的情感词及其权重信息,将其作为情感先验知识,融入到产生式分类模型...     

一种用于结构化文档检索的贝叶斯网络

分析结构化文档的表示方法及检索特点,对一种用于结构化文档检索的贝叶斯网络进行研究。讨论该贝叶斯网络的构造方法、概率估计及推理过程。用网络节点表示文档索引术语和结构单元,用弧表示术语和结构单元的隶属关系,根据TF-IDF方法估计各节点的先验概率,当给定一个查询时,通过计算每个结构单元的条件概率得到该结构单元的相关值。实例验证了该贝叶斯网络的有效性。     

基于马尔科夫随机场的图像锐化方法

针对常规的反锐化掩膜方法对图像进行增强时会放大噪声的问题,提出一种基于马尔科夫随机场理论的图像锐化方法,将Thin Plate先验能量函数引入到反锐化掩膜方法中。实验结果表明,该方法在突出图像边缘细节的同时,可有效抑制噪声。主观视觉效果和客观评价结果表明,该方法的处理效果较好。     

基于差分算子边缘约束先验的图像盲复原算法

为了有效克服图像在复原过程的边缘退化、振铃效应等影响,使空间域的边缘先验信息能够灵活的添加到图像复原算法中,依据差分算子描述检测边缘的特性,将数字摄影测量学中的拉普拉斯算子、Robert梯度算子、方向差分算子作为一种新的边缘约束先验引入到图像复原过程中,同时使用Toeplitz矩阵实现图像在空间域解卷积的过程,提出了一种以差分算子为边缘约束先验的空域图像复原算法。模拟数据的实验结果体现了更多的细节信息,相关评价指标表明了该算法的有效性。     

单一图像的快速去雾算法

为了改善雾天环境下退化图像的视觉效果,结合雾天成像模型和暗原色先验的规律,提出了一种快速的单一图像去雾算法。利用暗原色先验对大气参数进行估计,选取图像中满足条件的像素点进行透射率的估计,在此基础之上利用多元线性回归分析得到回归方程,并据此计算图像中其它像素点的透射率,从而大大减小了图像透射率估计运算量,最后根据大气参数和透射率计算得到清晰化的图像。实验结果表明,该算法相对于传统的单一图像去雾方法具有处理速度快和效果好的特点。     

基于熵的视图安全性判定算法

在概率数据库中,发布视图和隐私信息的不确定性是影响视图安全性判定效果的主要因素,针对以上问题,用熵来描述发布视图和隐私信息的不确定性,使视图安全性判定问题转化为熵的计算问题,提出一个基于熵的视图安全性判定定理,给出基于熵的视图安全性判定算法。实验结果证明,该算法能有效地对发布视图进行安全性判定。     

基于多选集的视图安全发布算法

现有的视图发布安全判定算法缺少对在先验知识条件下视图发布安全性的研究。将先验知识作为安全判定条件引入到视图安全发布算法中,提出符合多选集的视图安全发布方法,采用元组概括过滤思想形成视图,避免数据精度损失过大,在可接受的相对安全范围内发布视图,给出具体实现算法Multi—set算法。实验证明该算法能有效提高发布视图安全性。     

Modeling of electromagnetic band gap structures: A review

This article represents a comprehensive review of the research carried out on analytical and numerical methods modeling of electromagnetic band‐gap (EBG) structures used in around last two decades. Because of the unique characteristics of the surface wave reduction as well as perfect magnetic conductor (PMC) like behavior, the EBG structures have created their separate existence in antenna engineering society. These structures are being widely used in designing of several microwave planar circuits including printed antennas, printed microwave filters, etc. The purpose of this article is to present an inclusive review of analytical methods as well as numerical methods in the context of modeling of EBG‐structures. Such a review process is rarely carried out in the open literature to the best of authors' knowledge. The review exercise might be helpful to the researchers working on modeling of EBG‐structures as well as of EBG‐structured printed antennas, microwave planar filters, etc.     

A shape prior constraint for implicit active contours

We present a shape prior constraint to guide the evolution of implicit active contours. Our method includes three core techniques. Firstly, a rigid registration is introduced, using a line search method within a level set framework. The method automatically finds the time step for the iterative optimization processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances the impact of the shape prior versus the image guidance of the active contour. A mixed stopping condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our method is completely non-parametric and avoids taking linear combinations of non-linear signed distance functions, which would cause problems because distance functions are not closed under linear operations. Experimental results show that our method is able to extract the desired objects in several circumstances, namely when noise is present in the image, when the objects are in slightly different poses and when parts of the object are invisible in the image.     

Approximate Predictive Densities and Their Applications in Generalized Linear Models

Exact calculations of model posterior probabilities or related quantities are often infeasible due to the analytical intractability of predictive densities. Here new approximations for obtaining predictive densities are proposed and contrasted with those based on the Laplace method. Our theory and a numerical study indicate that the proposed methods are easy to implement, computationally efficient, and accurate over a wide range of hyperparameters. In the context of GLMs, we show that they can be employed to facilitate the posterior computation under three general classes of informative priors on regression coefficients. A real example is provided to demonstrate the feasibility and usefulness of the proposed methods in a fully Bayes variable selection procedure.