基于目标特征的动态支持向量机研究

研究了将待识别目标特征与SVM相结合的动态SVM。提出一种以目标特征与每个训练样本间的距离度量SVM软间隔优化问题中惩罚参数C的方法,可根据两者间距离大小赋予每个训练样本一个惩罚参数,从而更好地体现了不同训练样本对于待识别目标特征的价值。然后,根据各样本惩罚参数的大小重构动态训练样本集,训练以待识别目标特征的分类为核心任务的动态SVM,寻求以目标特征为中心的局部空间的最优分类面。并对两类水声目标的识别情况进行了比较,实验表明效果好于SVM和k-近邻分类器。     

基于支持向量机的InSAR干涉图相位解缠法

提出了一种基于支持向量机的InSAR干涉图区域分割相位解缠法,以支持向量机为分类工具,利用干涉图中的残差点、相位导数方差等相位属性对干涉图中像元分类,把干涉图分割成高质量非掩模区域和低质量掩模区域.先用改进的Itoh方法对非掩模区域解缠,然后应用区域生长策略对掩模区域解缠.最后用真实的和模拟的干涉相位图试验表明算法比现有的传统算法如枝切法等InSAR干涉图相位解缠方法更有效.     

Short‐term forecasts of euro area real GDP growth: an assessment of real‐time performance based on vintage data

Forecasters commonly predict real gross domestic product growth from monthly indicators such as industrial production, retail sales and surveys, and therefore require an assessment of the reliability of such tools. While forecast errors related to model specification and unavailability of data in real time have been assessed, the impact of data revisions on forecast accuracy has seldom been evaluated, especially for the euro area. This paper proposes to evaluate the contributions of these three sources of forecast error using a set of data vintages for the euro area. The results show that gains in accuracy of forecasts achieved by using monthly data on actual activity rather than surveys or financial indicators are offset by the fact that the former set of monthly data is harder to forecast and less timely than the latter set. These results provide a benchmark which future research may build on as more vintage datasets become available. Copyright © 2008 John Wiley & Sons, Ltd.     

Real‐time or current vintage: does the type of data matter for forecasting and model selection?

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real‐time monetary aggregate M3 (1977–2000) and residential mortgage credit (1975–1998). The forecasting method we use is multi‐step‐ahead non‐adaptive forecasting. Copyright © 2008 John Wiley & Sons, Ltd.     

Modelling and forecasting time series sampled at different frequencies

This paper discusses how to specify an observable high‐frequency model for a vector of time series sampled at high and low frequencies. To this end we first study how aggregation over time affects both the dynamic components of a time series and their observability, in a multivariate linear framework. We find that the basic dynamic components remain unchanged but some of them, mainly those related to the seasonal structure, become unobservable. Building on these results, we propose a structured specification method built on the idea that the models relating the variables in high and low sampling frequencies should be mutually consistent. After specifying a consistent and observable high‐frequency model, standard state‐space techniques provide an adequate framework for estimation, diagnostic checking, data interpolation and forecasting. An example using national accounting data illustrates the practical application of this method. Copyright © 2008 John Wiley & Sons, Ltd.     

基于短语结构和词语词性相结合的情感分类方法

针对传统文本分类方法忽略词语间的语义特征的问题,并为了改善输入文本的表示质量,提出一种基于短语结构和词语词性相结合的情感分类方法.该方法首先通过短语结构优化分词,可以更好地提取文本特征;其次利用Word2vec工具训练词语和词性相结合的文本语料库得到词向量模型,解决了Word2vec无法识别一词多义的问题;最后通过SVM算法对文本进行情感分类.实验结果表明,该算法能够提高文本情感分类的正确性.该方法对舆情监控、股票市场行情预测和了解消费者对产品的偏好等具有较高的实用性.     

基于SVM的分类预测

简单介绍了SVM的理论背景，详细介绍了基于SVM的分类预测方法。给出了基于SVM的分类预测技术的性能测试结果。指出该分类预测技术可将实际问题通过非线性变换转换到高维的特征空间，在高维空间中构造线性辨别函数来实现原空间中非线性辨别函数。     

基于人为误差的支持向量机——4E-SVM

传统的支持向量机是将分类问题转化成二次规划问题来解决的。针对传统的支持向量机算法及其变形算法忽略了训练集数据含有较大人为误差参与时其算法精度所存在的保障问题,提出了基于人为误差的支持向量机（artificial error-support vector machine以下称AE—SVM）的基本理论,并建立了AE—SVM的理论模型。该模型是C—SVM模型的改进和推广。     

用球结构的支持向量机解决多分类问题

支持向量机是从统计学习理论中导出的，从研究二分类开始，发展至今，虽然提出了很多多类别分类的相关算法，但都各有不足之处。提出基于球结构的支持向量算法，适用于规模比较庞大的多类别问题，并对其性质进行了讨论。     

基于和声搜索算法的支持向量机参数优化

支持向量机是建立在统计学理论基础上,以结构风险最小为原则的一种机器学习算法,能够很好地解决小样本、高维数、非线性等问题,被广泛地应用于模式识别、函数估计及回归预测等领域.但是支持向量机性能的高低往往取决于其相关参数的正确选择.为提高优化参数的精度及效率,利用和声搜索算法的全局寻优能力,对支持向量机的惩罚参数及核参数进行优化选择.通过4个标准UCI数据集的仿真实验,结果表明本算法不仅减少了搜索时间,而且所获得的参数能大幅提高支持向量机的性能和预测精度,提高了泛化能力.