复杂性测度特征在肺部HRCT图像分析中的应用

研究了分形维和 Lempel- Ziv(LZ)复杂性两类基于视觉复杂性的图像特征在自动区分高分辨率 CT(HRCT)上磨玻璃影 (GGO)与正常区域的表现 .研究样本包括 86个 1 5× 1 5大小的矩形感兴趣区 (ROI) ,其中 44个正常 ,42个 GGO.将从这些 ROI中提取的分形维特征和 LZ复杂性特征作为输入对线性分类器训练并对其分类性能进行评估 .结果表明 ,若将两类特征单独作为分类器的输入 ,相应的 ROC曲线下面积分别为 0 .837和 0 .90 3;当用回代法训练和测试分类器时 ,分别有 75.6%和 79.1 %的 ROI被正确分类 ,而用刀切法时 ,ROI被正确分类的比率相同 .若将两类特征的组合作为分类器输入 ,相应的 ROC曲线下面积提高到 0 .969,而总的分类正确率亦达 91 .9%     

赛程问题分治算法

对于单循环赛的比赛日程安排问题，给出了可读性较好，时空复杂度仅为平方阶的分而治之算法。     

技术发展的复杂性机制初探

技术发展受到多个张力的作用,这些张力交织在一起,发生非线性相互作用.当这种非线性相互作用可以近似为线性相互作用时,因果之间一一对应,技术呈现线性发展;当这种非线性相互作用为非线性相关,因果之间一一对应时,技术呈现非线性连续发展;当这种非线性相互作用为非线性相关,因果之间一多对应时,技术呈现发展的多种可能性,主体可以进行选择,技术发展表现出不确定性和偶然性.     

KNA算法计算单零点多项式全部零点的复杂性

证明用KNA算法计算n次单零点多项式全部零点所需的多项式计值次数不超过O(n~3 log_2(n/ε)),其中ε是计算精度。     

复杂决策系统研究——框架及其方法

界定了一种复杂适应性系统——复杂决策系统 ,其中影响个体决策的环境正是由大量个体决策结果构成的 .我们对它的构成、主要特征和研究方法进行了详细分析 ,建立了复杂决策系统的一种面向对象的模型和研究框架.     

Shell排序及改进算法的算法复杂性估计

本文用参数设计方法进行非线性回归估计。给出shell排序[1][2]和本人改进后的shell算法复杂性估计式。结果表明二者的算法复杂性已接近排序算法的理论下界——O(NLog(N))~*。     

Efficient algorithms for agglomerative hierarchical clustering methods

Whenevern objects are characterized by a matrix of pairwise dissimilarities, they may be clustered by any of a number of sequential, agglomerative, hierarchical, nonoverlapping (SAHN) clustering methods. These SAHN clustering methods are defined by a paradigmatic algorithm that usually requires 0(n ³) time, in the worst case, to cluster the objects. An improved algorithm (Anderberg 1973), while still requiring 0(n ³) worst-case time, can reasonably be expected to exhibit 0(n ²) expected behavior. By contrast, we describe a SAHN clustering algorithm that requires 0(n ² logn) time in the worst case. When SAHN clustering methods exhibit reasonable space distortion properties, further improvements are possible. We adapt a SAHN clustering algorithm, based on the efficient construction of nearest neighbor chains, to obtain a reasonably general SAHN clustering algorithm that requires in the worst case 0(n ²) time and space.Whenevern objects are characterized byk-tuples of real numbers, they may be clustered by any of a family of centroid SAHN clustering methods. These methods are based on a geometric model in which clusters are represented by points ink-dimensional real space and points being agglomerated are replaced by a single (centroid) point. For this model, we have solved a class of special packing problems involving point-symmetric convex objects and have exploited it to design an efficient centroid clustering algorithm. Specifically, we describe a centroid SAHN clustering algorithm that requires 0(n ²) time, in the worst case, for fixedk and for a family of dissimilarity measures including the Manhattan, Euclidean, Chebychev and all other Minkowski metrics.This work was partially supported by the Natural Sciences and Engineering Research Council of Canada and by the Austrian Fonds zur Förderung der wissenschaftlichen Forschung.     

The distribution search: An O (n) expected time search

Provided an algorithm for the distribution search and proves the time complexity of the algorithm. This algorithm uses a mathematical formula to searchn elements in the sequence ofn elements in O (n) expected time, and experimental reesult proves that distribution search is superior to binary search. Xu Xusong: born in June 1945, Professor