On Nonparametric Residual Variance Estimation

In this paper, the problem of residual variance estimation is examined. The problem is analyzed in a general setting which covers non-additive heteroscedastic noise under non-iid sampling. To address the estimation problem, we suggest a method based on nearest neighbor graphs and we discuss its convergence properties under the assumption of a Hölder continuous regression function. The universality of the estimator makes it an ideal tool in problems with only little prior knowledge available.     

基于邻居信息的蓝牙分散网动态构建算法

蓝牙网络的低功耗,无线通讯特性使其在无线网络应用领域有着广泛前景。对于组网算法的探讨有利于促进其在实际中的应用。根据设备的邻居结点信息,递归地在同一过程中完成匹克网和分散网的创建。这种组网算法避免了随机组网的盲目性,提高了分散网的稳定性和灵活性,使匹克网的分布尽量均匀,减小匹克网间的相互干扰,从而提高了网络的通讯效率。     

Collocation Dictionary Optimization Using WordNet and k-Nearest Neighbor Learning

In machine translation, collocation dictionaries are important for selecting accurate target words. However, if the dictionary size is too large it can decrease the efficiency of translation. This paper presents a method to develop a compact collocation dictionary for transitive verb–object pairs in English–Korean machine translation without losing translation accuracy. We use WordNet to calculate the semantic distance between words, and k-nearestneighbor learning to select the translations. The entries in the dictionary are minimized to balance the trade-off between translation accuracy and time. We have performed several experiments on a selected set of verbs extracted from a raw corpus of over 3 million words. The results show that in real-time translation environments the size of a collocation dictionary can be reduced up to 40% of its original size without significant decrease in its accuracy.     

Efficient Regular Data Structures and Algorithms for Dilation, Location, and Proximity Problems

In this paper we investigate data structures obtained by a recursive partitioning of the multi- dimensional input domain into regions of equal size. One of the best known examples of such a structure is the quadtree . It is used here as a basis for more complex data structures. We also provide multidimensional versions of the stratified tree by van Emde Boas [vEB]. We show that under the assumption that the input points have limited precision (i.e., are drawn from the integer grid of size u ) these data structures yield efficient solutions to many important problems. In particular, they allow us to achieve O(log log u) time per operation for dynamic approximate nearest neighbor (under insertions and deletions) and exact on-line closest pair (under insertions only) in any constant number of dimensions. They allow O(log log u) point location in a given planar shape or in its expansion (dilation by a ball of a given radius). Finally, we provide a linear time (optimal) algorithm for computing the expansion of a shape represented by a region quadtree. This result shows that the spatial order imposed by this regular data structure is sufficient to optimize the operation of dilation by a ball. Received January 19, 1999; revised November 4, 1999.     

基于近邻估计的年径流预测动态 联系数回归模型

集对分析理论为处理确定、不确定系统提供了新的途径,根据集对分析理论建立起来的预测联系数回归模型可以明显改善回归模型的预测精度。对于预测因子结构具有的动态性,文中将利用近邻估计,通过计算各个预测因子的变异系数,来判断预测因子在某次预测中处于强势或者弱势,进而动态地选择预报功能大的强势因子,消除对预报起负面作用的弱势因子的作用,这样很好地体现了预测因子结构中具有的动态性。基于此建立了基于近邻估计的年径流预测动态联系数回归模型（NNE-DCNR）。结果说明：用NNE-DCNR去预测年径流量,预测精度比常用预测方法有显著提高,在水文水资源的预测中具有推广应用价值     

一种快速的反向k近邻查找算法及其改进

提出一种快速的反向k近邻查找算法,该方法利用现代计算机具有外存便宜、运行速度快的特点,预先计算数据之间的距离,并组织为数据索引块存储于外存,由计算机在空闲时自动进行维护.在进行反向最近邻查询时,只需读入相应的索引块,就可进行直接查询,其时间复杂度为O(N),而且不受k的影响.为减少索引块的读取时间,提出一种改进方法来有效地压缩索引块,仅用必要的二进制位来存储对象之间的距离,并将冗余减少到最低水平,提高了算法的效率.最后通过实验分析评估算法的有效性和效率.     

自适应SINR门限的自动邻区关系生成算法

在长期演进(LTE)系统中,为保证终端通信的正常进行,每个小区需要维护一张邻区列表,该表决定终端切换的范围和方向.传统的相邻小区列表配置是网络工程师根据现场勘测情况进行配置,这样不仅会消耗大量的时间和人力,而且不具有全网统计性.笔者提出一种基于自适应SINR门限的自动邻区关系生成算法.该算法引入了自适应SINR门限以及"投票模型",充分考虑到了LTE网络中的小区类型,并能有效反映实际的网络环境,最终实现动态的相邻小区列表管理.通过仿真评估不同的SINR取值对相邻小区列表的冗余率和准确率的影响,获取了该算法的增益.     

Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform

A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.     

尺度与颜色不变性图像特征描述

尺度不变特征变换是目前公认的鲁棒性最强的图像特征描述方法之一,在尺度不变性和几何不变性方面具有较好的特性,但该方法主要适用于灰度图像,对图像颜色的区分能力不强,因此,一些对象可能会因为颜色的不同而被错误的区分.另外,尺度不变特征变换对关键点局部范围内描述子主方向的依赖性非常强,直接决定了匹配的正确率,但是研究表明,主方向分配产生的误差仅有三分之二左右能控制在[-20。,+20。]范围内,因此部分特征会有三分之一的概率因为主方向分配的误差较大而不能正确匹配.针对以上两个问题,本文提出了一种具有颜色和尺度不变性的局部特征描述方法,颜色不变性通过将RGB图像转换到高斯颜色模型下实现,特征描述过程中不再分配主方向,而用局部相对方向,尺度不变性通过构建高斯金子塔实现.实验选取阿姆斯特丹数据集图像进行了测试,结果表明本文方法比传统尺度不变特征变换方法,在特征点的数目、分布均匀性以及匹配精度方面均有所提高.     

基于最近邻相似度的孤立点检测及半监督聚类算法

传统的聚类算法是一种无监督的学习过程,聚类的精度受到相似性度量方式以及数据集中孤立点的影响,并且算法也没有很好的利用先验知识,无法体现用户的需求。因此提出了基于共享最近邻的孤立点检测及半监督聚类算法。该算法采用共享最近邻为相似度,根据数据点的最近邻居数目来判断是否为孤立点,并在删除孤立点的数据集上进行半监督聚类。在半监督聚类过程中加入了经过扩展的先验知识,同时根据图形分割原理对数据集进行聚类。文中使用真实的数据集进行仿真,其仿真结果表明,本文所提出的算法能有效的检测出孤立点,并具有很好的聚类效果。