半参数模型误差为鞅差序列的r阶矩相合性

利用偏残差法并综合最小二乘法,给出了半参数回归模型中参数β和非参数g(t)的、(t),在误差为鞅差序列时,得到了、(t)的r(r≥2)阶矩相合性.     

Arbitrarily shaped multiple spatial cluster detection for case event data

An original method is proposed for spatial cluster detection of case event data. A selection order and the distance from the nearest neighbour are attributed to each point, once pre-selected points have been taken into account. This distance is weighted by the expected distance under the uniform distribution hypothesis. Potential clusters are located by modelling the multiple structural change of the distances on the selection order and the best model (containing one or several potential clusters) is selected using the double maximum test. Finally a p-value is obtained for each potential cluster. With this method multiple clusters of any shape can be detected.     

Selective Sampling for Nearest Neighbor Classifiers

Most existing inductive learning algorithms work under the assumption that their training examples are already tagged. There are domains, however, where the tagging procedure requires significant computation resources or manual labor. In such cases, it may be beneficial for the learner to be active, intelligently selecting the examples for labeling with the goal of reducing the labeling cost. In this paper we present LSS—a lookahead algorithm for selective sampling of examples for nearest neighbor classifiers. The algorithm is looking for the example with the highest utility, taking its effect on the resulting classifier into account. Computing the expected utility of an example requires estimating the probability of its possible labels. We propose to use the random field model for this estimation. The LSS algorithm was evaluated empirically on seven real and artificial data sets, and its performance was compared to other selective sampling algorithms. The experiments show that the proposed algorithm outperforms other methods in terms of average error rate and stability.     

Rectilinear steiner tree heuristics and minimum spanning tree algorithms using geographic nearest neighbors

We study the application of the geographic nearest neighbor approach to two problems. The first problem is the construction of an approximately minimum length rectilinear Steiner tree for a set ofn points in the plane. For this problem, we introduce a variation of a subgraph of sizeO(n) used by YaO [31] for constructing minimum spanning trees. Using this subgraph, we improve the running times of the heuristics discussed by Bern [6] fromO(n ² log n) toO(n log² n). The second problem is the construction of a rectilinear minimum spanning tree for a set ofn noncrossing line segments in the plane. We present an optimalO(n logn) algorithm for this problem. The rectilinear minimum spanning tree for a set of points can thus be computed optimally without using the Voronoi diagram. This algorithm can also be extended to obtain a rectilinear minimum spanning tree for a set of nonintersecting simple polygons.The results in this paper are a part of Y. C. Yee's Ph.D. thesis done at SUNY at Albany. He was supported in part by NSF Grants IRI-8703430 and CCR-8805782. S. S. Ravi was supported in part by NSF Grants DCI-86-03318 and CCR-89-05296.     

Soft sensor modeling method based on hybrid model of nearest neighbor and neural network

Soft-sensing modeling can effectively solve the problems of large measurement lag, high price, and complex maintenance of online analytical instruments in the production process. At present, neural network based on data-driven is one of the main tools of soft sensor. In the process of modeling data collection, the collection of dominant variables is much more difficult than that of auxiliary variables, resulting in a large amount of unlabeled data. However, traditional soft sensor modeling methods ignore these unlabeled data and only use a small amount of labeled data for modeling, which has negative effect on the prediction accuracy of the model. To solve the problem of label missing, the nearest neighbor algorithm is used to pseudo label the unlabeled data. At the same time, a network structure is designed by combining convolution operation and gated recurrent unit neural network (GRU) to further utilize the unlabeled data, extract the dynamic feature from data at different time, and improve the prediction accuracy of the neural network. Finally, the method is applied to the prediction of propane concentration on the top of propylene distillation column. The results show that the model can solve the problem of label missing in the nonlinear dynamic system and has higher prediction accuracy.     

基于多尺度稀疏近邻图的近邻保持嵌入算法

针对近邻保持嵌入算法NPE中构造近邻图所存在的缺陷,提出了基于多尺度稀疏近邻图的近邻保持嵌入算法.对于每个待识别的人脸图片,该方法都建立一个具有九个尺度的图像金字塔,并且计算金字塔中每个尺度的图片与其他图片金字塔对应尺度的稀疏近邻.利用稀疏表示算法抗遮挡的特性,通过计算样本多尺度近邻的方法克服了传统方法丢失人脸图片二维结构的缺点.结果表明,该算法具有较强的鲁棒性,比传统的NPE算法具有更好的识别效果.     

基于斜平面平滑优化的半全局立体匹配

基于斜平面平滑的半全局匹配算法虽然能很好地处理深度不连续区域,减少视差断裂现象,具有很好的视差平滑效果.但此方法计算量大,并且若输入的左右图像对不是理想校正的,最终得到的视差图中可能会出现黑色小方块.针对上述问题,本文提出了一种基于斜平面平滑优化的双目立体视觉算法,优化了视差斜平面拟合过程,提高了斜平面平滑的运算速度;另,通过对初始视差图中无效的视差值进行临近插值填充,很好地解决了视差图中的方块现象;同时考虑到同一分割块内的像素梯度值应比较接近,对图像分割时用到的能量函数增加梯度项,使分割区域更加合理.实验结果表明,本文算法能取得更好的视差图,主观效果得到改善,运算速度提高约40%.     

面向WLAN室内定位的T检验样本容量优化方法

WLAN（Wireless Local Area Networks）室内定位已受到人们广泛的关注,而在离线指纹采集阶段常常容易造成位置指纹RSS数据采集的盲目性和不可靠性,并忽略所需采集RSS（Received Signal Strength）样本容量与定位性能的关系.为了解决这一问题,本文提出一种面向WLAN室内定位的T检验样本容量优化方法.该方法在离线阶段利用OC（Operating Characteristics）函数优化指纹数据库允许的最小RSS样本容量,而在在线阶段利用T检验方法对目标终端进行粗定位,并进而提出基于T检验的KNN（K-Nearest Neighbour）算法以完成对目标终端的精定位.此方法用有限的样本容量获得较稳定的定位性能分析结果,显著地减少了大量的人力和时间开销.     

多变量时滞计算的最大独立互相关算法

为解决相空间重构中多变量时滞参数难以同时选择的问题,提出一种基于最大独立互相关的时滞计算方法.将响应变量序列分段处理;对各段曲面拟合并将观测变量序列代入拟合函数;迭代运算至互相关最小,得到最优时滞.对最大独立互相关算法与遗传神经网络、互信息法、极大联合熵法进行对比实验,并引入联合递归图与共有近邻比值法作为评价方法,结果表明:最大独立互相关算法克服了传统方法的不足.选取某矿井下进风巷、上隅角、工作面和回风巷4个位置瓦斯浓度的真实数据进行四变量最优时滞选择实验并与互信息法对比,最大独立互相关算法的时滞计算结果为16-3-10-11,共有近邻比为0.58,联合递归密度为0.34%,优于传统方法.提出算法能够应用于实际多变量分析,具有一定实用价值.