一种改进的结合K近邻法的SVM分类算法

在对支持向量机在超平面附近容易对测试样本造成错分进行研究的基础上,改进了将支持向量机分类和k近邻分类相结合的方法,形成了一种新的分类器.在分类阶段计算待识别样本和最优分类超平面的距离,如果距离差大于给定阈值可直接应用支持向量机分类,否则用最佳距离k近邻分类.数值实验表明,使用支持向量机结合最近邻分类的分类器分类比单独使用支持向量机分类具有更高的分类准确率.     

Super‐resolution of the undersampled and subpixel shifted image sequence by a neural network

Numerous approaches to super‐resolution (SR) of sequentially observed images (image sequence) of low resolution (LR) have been presented in the past two decades. However, neural network methods are almost ignored for solving SR problems. This is because the SR problem traditionally has been regarded as the optimization of an ill‐posed large set of linear equations. A designed neural network based on this has a large number of neurons, thereby requiring a long learning time. Also, the deduced cost function is overly complex. These defects limit applications of a neural network to an SR problem. We think that the underlying meaning of the SR problem should refer to super‐resolving an imaging system by image sequence observation, instead of merely improving the image sequence itself. SR can be regarded as a pattern mapping from LR to SR images. The parameters of the pattern mapping can be learned from the imaging process of the image sequence. This article presents a neural network for SR based on learning from the imaging process of the image sequence. In order to speed up the convergence, we employ vector mapping to train the neural network. A mapping vector is composed of some neighbor subpixels. Such a well‐trained neural network has powerful generalization ability so that it can be used directly to estimate the SR image of the other image sequences without learning again. Our simulations show the effectiveness of the proposed neural network. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 8–15, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20001     

基于有损连接的个性化隐私保护

数据表的k-匿名化(k-anonymization)是数据发布环境下保护数据隐私的一种重要方法,在此基础上提出的(,)-匿名模型则是有效的个性化隐私保护方法,泛化/隐匿是实现匿名化的传统技术,然而该技术存在效率低,信息损失量大等缺陷。针对上述问题,引入有损连接的思想,提出了基于贪心策略的(,)-匿名聚类算法,该方法通过准标识符属性和敏感属性间的有损连接来保护隐私数据。实验结果表明,与泛化/隐匿方法相比,该方法在信息损失量和时间效率上具有明显的优势,可以获得更好的隐私信息保护。     

一种新颖的径向基函数(RBF)网络学习算法

以提高RBF网络泛化能力为着眼点，提出了一种新型的网络结构自适应学习算法．该算法采用衰减聚类半径的聚类算法来确定初始的隐层结构，然后通过调整包含样本类别信息的扩展聚类不纯度来修正隐层结构，直至满足所有扩展聚类不纯度均小于等于不纯度均值以及所有扩展聚类方差均不超过方差均值这两个条件．这样就确定了隐层的最终结构．在确定隐层结构之后，采用反向传播算法来训练隐层与输出层之间的连接权重．经双螺旋线问题仿真试验验证，该算法确实具有较强的泛化能力．     

一种改进的推广能力衡量准则

通过对支撑向量机推广能力的分析，提出了一种构造性的与样本分布有关的推广能力衡量准则．该准则与统计学习理论中的推广能力准则具有几何上的一致性，由样本的二阶统计量构成，比已有的完全不依赖于样本分布的推广能力上界更能反映学习过程的收敛性和收敛速率．较为重要的一点是该准则在学习过程之前是可处理的，所以它可以用作所有分类器中数据预处理的准则，同时也可以为支撑向量机模型的选择提供依据．文中最后给出的人工及实际的例子也很好地说明了该准则的合理性．     

通用的多协议标签交换GMPLS的信令技术

GMPLS信令系统是在MPLS信令协议的基础上扩展而来的，本从通用性，流量工程和生存性三个方面详细介绍了GMPLS信令系统功能的增强，最后对GMPLS信令协议在应用中所面临的RSVP-TE和CR-LDP的选择问题进行了分析，给出了两种信令协议的对比情况，供在实际使用时参考。     

Incremental POP Learning

In recently proposed partial oblique projection (POP) learning, a function space is decomposed into two complementary subspaces, so that functions belonging to one of which can be optimally estimated. This paper shows that when the decomposition is specially performed so that the above subspace becomes the largest, a special learning called SPOP learning is obtained and correspondingly an incremental learning is implemented, result of which equals exactly to that of batch learning including novel data. The effectiveness of the method is illustrated by experimental results.     

Partial Oblique Projection Learning for Optimal Generalization

In some viewpoints, supervised learning is discussed in the framework of function approximation, which means that different criteria result in learning methods of different abilities in generalization [1]. From the standpoint of the original space to which the desired function belongs, projection-based criterion aims directly at the generalization ability[2]. The projection concept is applied to projection learning (PL), partial projection learning (PTPL), and averaged projection learning (A…     

Meta-analysis of coefficient alpha.

The meta-analysis of coefficient alpha across many studies is becoming more common in psychology by a methodology labeled reliability generalization. Existing reliability generalization studies have not used the sampling distribution of coefficient alpha for precision weighting and other common meta-analytic procedures. A framework is provided for a statistically grounded meta-analysis of coefficient alpha using its sampling distribution. Two empirical examples are offered to illustrate these methods, and limitations of reliability generalization are described. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Choosing the best method for local validity estimation: Relative accuracy of meta-analysis versus a local study versus Bayes-analysis.

This study assessed the relative accuracy of 3 techniques--local validity studies, meta-analysis, and Bayesian analysis--for estimating test validity, incremental validity, and adverse impact in the local selection context. Bayes-analysis involves combining a local study with nonlocal (meta-analytic) validity data. Using tests of cognitive ability and personality (conscientiousness) as predictors, an empirically driven selection scenario illustrates conditions in which each of the 3 estimation techniques performs best. General recommendations are offered for how to estimate local parameters, based on true population variability and the number of studies in the meta-analytic prior. Benefits of empirical Bayesian analysis for personnel selection are demonstrated, and equations are derived to help guide the choice of a local validity technique (i.e., meta-analysis vs. local study vs. Bayes-analysis). (PsycINFO Database Record (c) 2010 APA, all rights reserved)