改进的加权极速学习机

针对加权极速学习机人为固定权重可能会错失更优权重的问题,提出了改进的加权极速学习机。该方法的多数类的初始权重设为1,使用多数类与少数类样例数的比值作为少数类的初始权重,然后通过在多数类或者少数类中添加权重调节因子,从缩小和扩大两个方向去调节权重,最后通过实验结果选出最优的权重。实验分别使用原加权极速学习机、其他权重的极速学习机和新方法在改造的UCI数据集上进行比较。结果表明新方法无论是在F-mea-sure还是G-mean上都要优于其他加权极速学习机。     

基于变元加权的一种求解SAT问题的新方法

研究合取范式可满足性的SAT问题作为一个NP完全问题,在计算机科学及组合优化问题领域中有着中心课题的重要地位。由于其NP问题的性质决定了它尚无通用快速的完全算法,因此基于“实验算法学”的思想,按照平均性态而不是最坏情况性态的原则,对具有启发式策略的不完全算法的研究成为近年来大家关注与努力的焦点。该文正是立足于此,提出了“加权消元”这一种全新而且高效的算法。     

Borda数法在选优中的应用

在学校评选优秀分子,既要体现公正,又要顾及各方意见。如何解决这类问题?本文通过引入模糊数学中的Borda数法及加权的Borda数法,对此进行了阐述。并引入了数表,使方法的计算过程得到了简化,同时给出了验证综合排序正确与否的公式。     

校园卡充值点最优位置的数学模型研究

选址问题目前学术界已有较多的研究成果,但大多数是将总费用作为目标函数,一般要求事先给出网络结点的位置坐标,且无需考虑结点间的最短路程,旨在确定新的地理几何中心。而对已有网络,在不改变原有路径及各结点位置的条件下,以总路程最小为目标函数,在现有网络结点中寻找其中某些结点的最优位置却是一个新的研究课题。本文以某高校校园卡充值点为例,将校园示意图转化为赋权连通图,求得该连通图的邻接矩阵,利用Floyd算法及图论软件包构造一个最短路径矩阵,得到一个赋权完全图,利用穷举法或混合整数规划法及数学软件求解,得到各学院、楼栋、学生宿舍区到三个校园卡充值点的最短总路程及三个校园卡充值点的最优位置。     

我国城镇居民消费与收入关系分析——基于地理加权回归模型（GWR）的实证分析

空间数据一般具有空间非平稳性的特点,究其原因是地理空间上的区域缺乏均质性,存在高消费地区和低消费地区、中心（核心）和外围（边缘）等经济地理结构,从而导致我国城镇居民消费存在较大的空间差异性。对此,本文试图采用地理加权回归模型来分析我国城镇居民消费与收入的关系,从实证分析的结果提出几点建议和对策。     

基于加权影响矩阵的提篮拱二次调索方法研究

为避免大跨提篮拱吊杆索力二次调索过程中,结构内力、位移出现顾此失彼的情况。以一450 m特大跨钢箱提篮拱为工程背景,二次调索过程中对影响矩阵增加加权系数,通过MATLAB求解加权后方程的最小二乘解,经过少量的迭代运算,索力差值即趋于稳定。结果表明:实测索力与设计成桥索力的相对误差可控制在5%以内,完全满足规范限值的要求;在有限元计算过程中考虑几何非线性效应的方式,减小了几何非线性效应对结构的影响;作为一种加速收敛的方法,加权影响矩阵具有收敛速度较为均匀的特点。     

Spatial analysis and evaluation of a coal deposit by coupling AHP & GIS techniques

Nowadays, the evaluation of coal deposits becomes crucial, due to many uncontrollable factors, which affect the energy sector. A comparative evaluation of coal deposits is essential for their hierarchical classification regarding their sustainable exploitation, when compared to other coal deposits or competitive fuels, which may be used as alternative solutions for electricity generation. In this paper, a method for spatial analysis and evaluation of a lignite deposit is proposed, by creating four spatial key indicators via GIS analysis, which are then aggregated by applying a weighted linear combination. The analytical hierarchy process is applied to estimate the relative weights of the indicators, in order to perform a weighted cartographic overlay. Through the synthesis of the indicators, an overall, total spatial quality indicator is calculated. The weighted analysis was shown to be more effective compared to the unweighted one, because it can provide more reliable results regarding the exploitation of the examined lignite deposit. The implementation of GIS-based analytical hierarchy process in spatial analysis and evaluation of lignite deposits, in terms of sustainable exploitation, demonstrates that this method can be extensively applied for evaluating the economic potential of mineral deposits.     

Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting

Weighted voting is the commonly used strategy for combining predictions in pairwise classification. Even though it shows good classification performance in practice, it is often criticized for lacking a sound theoretical justification. In this paper, we study the problem of combining predictions within a formal framework of label ranking and, under some model assumptions, derive a generalized voting strategy in which predictions are properly adapted according to the strengths of the corresponding base classifiers. We call this strategy adaptive voting and show that it is optimal in the sense of yielding a MAP prediction of the class label of a test instance. Moreover, we offer a theoretical justification for weighted voting by showing that it yields a good approximation of the optimal adaptive voting prediction. This result is further corroborated by empirical evidence from experiments with real and synthetic data sets showing that, even though adaptive voting is sometimes able to achieve consistent improvements, weighted voting is in general quite competitive, all the more in cases where the aforementioned model assumptions underlying adaptive voting are not met. In this sense, weighted voting appears to be a more robust aggregation strategy.     

Enhanced soft subspace clustering integrating within-cluster and between-cluster information

While within-cluster information is commonly utilized in most soft subspace clustering approaches in order to develop the algorithms, other important information such as between-cluster information is seldom considered for soft subspace clustering. In this study, a novel clustering technique called enhanced soft subspace clustering (ESSC) is proposed by employing both within-cluster and between-class information. First, a new optimization objective function is developed by integrating the within-class compactness and the between-cluster separation in the subspace. Based on this objective function, the corresponding update rules for clustering are then derived, followed by the development of the novel ESSC algorithm. The properties of this algorithm are investigated and the performance is evaluated experimentally using real and synthetic datasets, including synthetic high dimensional datasets, UCI benchmarking datasets, high dimensional cancer gene expression datasets and texture image datasets. The experimental studies demonstrate that the accuracy of the proposed ESSC algorithm outperforms most existing state-of-the-art soft subspace clustering algorithms.     

加权马尔可夫链预测模型的优化方法及应用

借鉴自回归模型,采取添加随机项的方法对加权马尔可夫链预测模型进行优化,并把优化后的模型应用于郑州市年降雨量的预测,通过与原模型和自回归模型预测结果的对比分析发现,优化后的模型弥补了原模型预测结果缺乏波动性的不足,同时使预测结果不仅在精度上有一定的提高,而且在趋势上更接近于实测值.