基于主元分析与支持向量机的人脸识别方法

基于支持向量机（SVM）在处理小样本，高维数及泛化性能等强方面的优势，提出了一种基于主元分析（PCA）与SVM的人脸识别方法，利用PCA方法对人脸图像进行特征提取，再利用SVM与最近邻分类器相结合的策略对特征向量进行分类识别，剑桥ORL的人极数据库的仿真结构验证了本算法是有效的。     

How successful are dynamic factor models at forecasting output and inflation? A meta‐analytic approach

This paper uses a meta‐analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro‐area output and for euro‐area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre‐selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well. Copyright © 2008 John Wiley & Sons, Ltd.     

New evidence on the relation between return volatility and trading volume

In the empirical literature, it has been shown that there exists both linear and non‐linear bi‐directional causality between trading volumes and return volatility (measured by the square of daily return). We re‐examine this claim by using realized volatility as an estimator of the unobserved volatility, adopting a stationary de‐trended trading volume, and applying a more recent data sample with robustness tests over time. Our linear Granger causality test shows that there is no causal linear relation running from volume to volatility, but there exists an ambiguous causality for the reverse direction. In contrast, we find strong bi‐directional non‐linear Granger causality between these two variables. On the basis of the non‐linear forecasting modeling technique, this study provides strong evidence to support the sequential information hypothesis and demonstrates that it is useful to use lagged values of trading volume to predict return volatility. Copyright © 2009 John Wiley & Sons, Ltd.     

Diagnosis of Vocal Cord Paralysis in Anaesthesia

Vocal cord paralysis can occur as a complication o surgery or anaesthesia,if permanent is a significant clinica problem.Early detection is important to optimize the chance o repair,and avoid complications associated with an impaired swallow.An algorithm to detect altered vocal cord function was presented based on wavelet packet analysis(WPA) and suppor vector machines(SVM),and compared with the Hoarseness Diagram method(HDm),which was reported as an objective voice quality evaluation approach and could be u...     

丛式井钻井技术在陕北油田的应用

介绍了陕北油田丛式井施工特点，从井身剖面设计、井组防碰、井眼轨迹控制等方面，探讨了丛式井施工的重点和难点，并提出了保证井内安全的技术措施。     

Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model

China is a populous country that is facing serious aging problems due to the single‐child birth policy. Debate is ongoing whether the liberalization of the single‐child policy to a two‐child policy can mitigate China's aging problems without unacceptably increasing the population. The purpose of this paper is to apply machine learning theory to the demographic field and project China's population structure under different fertility policies. The population data employed derive from the fifth and sixth national census records obtained in 2000 and 2010 in addition to the annals published by the China National Bureau of Statistics. Firstly, the sex ratio at birth is estimated according to the total fertility rate based on least squares regression of time series data. Secondly, the age‐specific fertility rates and age‐specific male/female mortality rates are projected by a least squares support vector machine (LS‐SVM) model, which then serve as the input to a Leslie matrix model. Finally, the male/female age‐specific population data projected by the Leslie matrix in a given year serve as the input parameters of the Leslie matrix for the following year, and the process is iterated in this manner until reaching the target year. The experimental results reveal that the proposed LS‐SVM‐Leslie model improves the projection accuracy relative to the conventional Leslie matrix model in terms of the percentage error and mean algebraic percentage error. The results indicate that the total fertility ratio should be controlled to around 2.0 to balance concerns associated with a large population with concerns associated with an aging population. Therefore, the two‐child birth policy should be fully instituted in China. However, the fertility desire of women tends to be low due to the high cost of living and the pressure associated with employment, particularly in the metropolitan areas. Thus additional policies should be implemented to encourage fertility.     

Short‐term prediction of motorway travel time using ANPR and loop data

Travel time is a good operational measure of the effectiveness of transportation systems. The ability to accurately predict motorway and arterial travel times is a critical component for many intelligent transportation systems (ITS) applications. Advanced traffic data collection systems using inductive loop detectors and video cameras have been installed, particularly for motorway networks. An inductive loop can provide traffic flow at its location. Video cameras with image‐processing software, e.g. Automatic Number Plate Recognition (ANPR) software, are able to provide travel time of a road section. This research developed a dynamic linear model (DLM) model to forecast short‐term travel time using both loop and ANPR data. The DLM approach was tested on three motorway sections in southern England. Overall, the model produced good prediction results, albeit large prediction errors occurred at congested traffic conditions due to the dynamic nature of traffic. This result indicated advantages of use of the both data sources. Copyright © 2008 John Wiley & Sons, Ltd.     

一种基于正例的SVM分类方法

文本分类属于有指导的机器学习,而构造一个按兴趣分类的分类器,需要进行大量的预处理工作收集正负训练样例,但负样例的收集难度很大.为此,提出了一个只有正例并基于SVM的分类学习模型.实验表明,该学习模型满足了文本分类对于分类精度和速度的要求.     

Evaluation of correlation forecasting models for risk management

Reliable correlation forecasts are of paramount importance in modern risk management systems. A plethora of correlation forecasting models have been proposed in the open literature, yet their impact on the accuracy of value‐at‐risk calculations has not been explicitly investigated. In this paper, traditional and modern correlation forecasting techniques are compared using standard statistical and risk management loss functions. Three portfolios consisting of stocks, bonds and currencies are considered. We find that GARCH models can better account for the correlation's dynamic structure in the stock and bond portfolios. On the other hand, simpler specifications such as the historical mean model or simple moving average models are better suited for the currency portfolio. Copyright © 2007 John Wiley & Sons, Ltd.     

基于支持向量机的N型激波识别技术

超音速目标识别过程中，其产生的N型激波容易与爆炸波混淆在一起，从爆炸波中识别N型激波非常重要。本文提出一种从爆炸波中识别N型激波的技术，通过用5．56mm，7．62mm，12．7mm超音速枪弹做射击试验和TNT炸药爆炸试验，获取N型激波和爆炸波原始数据，进行了特征提取，并采用主成份分析（PCA）方法对特征数据进行压缩处理后，用支持向量机（SVM）方法进行分类识别。结果表明，文中提出的识别方法是可行的和有效的。