考虑产品特征属性的替代性需求预测方法

随着电商销售业务的高速发展, 对用户需求进行快速准确预测已成为重要的研究方向. 产品间的替代性对需求有一定影响作用, 且此方面的应用研究在不断深入. 为了提升需求预测精度, 基于畅销预测属性值排序, 利用邻近替代率估计方法, 并结合 Adaboost 预测模型, 构建出一种更优的考虑产品特征属性的替代性需求预测方法, 并通过实验证明该方法行之有效.     

助推钻地弹对混凝土目标侵彻效应的数值模拟

为设计助推钻地弹选择合适的助推方案,采用LS-DYNA程序进行了三维数值模拟研究。通过将动能弹侵彻混凝土数值模拟的结果与试验数据进行对比,确定了合理的有限元仿真模型,选取了合适的材料模型和参数。结合某结构助推钻地弹的研制工作,对助推钻地弹侵彻混凝土的效应进行了数值模拟,与试验数据进行比较,得出数值模拟能够较好地反映助推钻地弹对混凝土目标的侵彻效应。通过对各种助推方案的数值模拟,得出了选择助推方案的一般原则,为新型钻地弹的研制提供了指导。     

A detection strategy of multi-pose face in compressed domain

In this paper, we present a strategy to implement multi pose face detection in compressed domain. The strategy extracts firstly feature vectors from DCT domain, and then uses a boosting algorithm to build classifiers to distinguish faces and non-faces. Moreover. to get more accurate results of the face detection, we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers. Through comparing and analyzing results of some experiments on the synthetic data and the natural data, we can get more satisfied results by the strong classifiers than by the weak classifies.     

中国古典诗歌对美国现代诗歌的影响

中国古典诗歌对美国现代诗的影响，是中国文化西传产生的重要成果之一，是中西比较文学研究中一个重要的课题，它足足影响了欧美三代诗人的诗歌创作，本文试举例说明这些跨越时空的对话的主要模式和媒介，以及这种双向文化交流的现实意义。     

基于Adaboost的作战目标属性判定方法

传统作战目标属性判定主要采用指挥员现场判断的定性方法, 具有一定的主观性, 并且由于缺乏较为成熟固定的算法而难以纳入指挥平台中。针对此问题, 结合作战目标属性判定关键影响因素分析, 提出一种基于自适应提升(adaptive boosting, Adaboost)的作战目标属性判定方法。首先, 针对目标有效面积、目标配置区域面积等关键因素, 采用单层决策树算法构建弱分类器。然后, 利用Adaboost对弱分类器进行加权组合, 形成作战目标属性判定的强分类模型。最后, 进行了示例分析, 并与决策树、支持向量机和人工神经网络3种属性判定方法进行对比仿真实验, 证明了所提方法的正确性和优越性。     

Improving moving average trading rules with boosting and statistical learning methods

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.     

XGBoost启发的双向特征选择算法

针对特征选择过程中特征评价指标单一性的问题, 基于集成学习中的极端梯度提升算法, 提出一种新的特征选择算法. 该算法首先应用极端梯度提升算法中构建集成树模型的指标作为特征选择的特征重要性度量指标, 然后利用一种新的双向搜索策略, 权衡了多种特征重要性对结果的影响, 并优化了评价过程的效率. 通过11个不同维度的标准数据集进行测试, 实验结果表明, 该算法能增加特征子集的多样性, 加快特征选择的速度, 并在中维和低维数据集上均具有较高的计算效率, 且能处理高维数据集.     

应用于WSN的0.5 V 4.8 GHz CMOS LC VCO设计

为设计一个可应用于无线传感网的0.5 V 4.8 GHz CMOS LC压控振荡器,采用传统差分负阻结构的电感电容VCO核心电路,添加开关电容阵列增大VCO的调谐范围,利用升压电路和反相器的组合提高控制信号产生电路的性能,通过调节负阻管的宽长比等方法来优化VCO的相位噪声性能,保证VCO能在0.5 V的低供电电压下稳定工作,相位噪声达到-119.3 dBc/Hz@1 MHz,VCO的频率调谐范围为4.3~5.3 GHz,相位噪声小于-115 dBc/Hz@1 MHz,最低可达-121.2 dBc/Hz@1 MHz,核心电路电流约为2.6 mA,满足无线传感网的应用要求。     

基于稀疏栅格优化的蜂窝车联网定位算法

蜂窝车联网（cellular-V2X,C-V2X）中的定位方案是车路协同与车联网业务发展的重要技术途径之一。目前基于基站、卫星等诸多定位方案,在车联网业务以及车路协同场景中常会遇到定位精度、定位处理时延、部署成本等诸多方面的挑战。针对这些问题,文章对已有栅格定位算法进行优化,提出一种基于统计信息网格（statistical information grid,STING）的稀疏栅格优化算法和基于极端梯度提升（extreme gradient boosting decision tree,XGBoost）进行指纹定位的车联网指纹定位算法。从栅格优化的角度出发,相较于传统指纹定位方法在定位精度和计算速率方面进行了优化,使其更适应于车路协同场景。该算法为目前的车联网定位提供了一种有效的定位方法。     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.