相关向量机及在说话人识别应用中的研究

对基于相关向量机和高斯混合模型的说话人识别算法的模型和特征空间进行了一系列的研究。与一些基于语音帧的说话人识别算法相比,该算法将GMM算法作为底层的语音特征提取,从而实现对语音整体上的处理,对常用的两种语音特征美尔频率倒频系数和瞬时频率的表现进行了对比研究;同时,该算法充分利用了相关向量机的所提供的高泛化性、核函数功能和结果的高稀疏性。基于Chains和AHUMADA两个专门用于说话人识别的语音库的仿真表明,该算法在减少相对误差和减少计算量方面有较大的优势。     

贸易开放度、货币供应量与中国通货膨胀关系的实证检验

在全球化背景下,贸易开放度和通货膨胀的关系成为国际经济学的热点I＇-7题。本文运用1996年到2008年的月度时间序列数据,在VAR模型的基础上,研究了贸易开放度、货币供应量与通货膨胀的动态关系。结果表明：贸易开放度、货币供应量是影响我国通货膨胀的重要因素,贸易开放度的提高,增加了广义货币供应量,进一步形成物价上涨的压力。     

多部门经济平衡增长研究

本文给出了两种有消费的动态投入产出经济模型,并讨论了生产、消费和投入之间的相互关系及它们随时间变化的特征。     

从向量函数微分去看切丛上的联络

本文利用向量函数的微分 ,通过微分算子的方式 ,给出了将基本形态、微分与导数概念自然地抽象提升到切丛上 ,进而到余切丛、张量丛上成为通常的联络的一认识方法。有利于微分几何中联络工具的理解     

面向JIT制造的零部件配送中心货位优化研究

面向JIT生产的零部件配送具有种类多、批次多、批量少、出入库时间分散等特点,使得有效分配配送中心的存储货位成为一个难题.将存储货位分配问题抽象为三角形装箱问题,构建了考虑时间维度的货位分配模型,设计了求解该问题的启发式算法.应用某知名汽车装配企业零部件配送中心的实际数据,对货位分配优化模型及算法进行了验证,实证数据表明模型及算法能够有效降低运营成本,提高存储空间利用率.     

Sequential monitoring of high-dimensional time series

In the paper we derive new types of multivariate exponentially weighted moving average (EWMA) control charts which are based on the Euclidean distance and on the distance defined by using the inverse of the diagonal matrix consisting of the variances. The design of the proposed control schemes does not involve the computation of the inverse covariance matrix and, thus, it can be used in the high-dimensional setting. The distributional properties of the control statistics are obtained and are used in the determination of the new control procedures. Within an extensive simulation study, the new approaches are compared with the multivariate EWMA control charts which are based on the Mahalanobis distance.     

On the block lu decomposition of block toeplitz matrices using the vector yule-walker equations

It is shown that the inverse of a block Toeplitz matrix can be factored into a product of an upper block triangular, a block diagonal and a lower block triangular matrices, where the component matrices consist of the solutions of the forward and backward extended Yule-Walker equations. Also, a recursive algorithm is presented to decompose nested Toeplitz matrices, which is a generalization of the Levinson-Durbin algorithm. Its derivation is elementary. The decomposition and the algorithm are useful for solving a block Toeplitz system of simultaneous equations, particularly, which appears in the vector autoregressive moving-average model analysis.     

A Study on SMO Algorithm for Solving ϵ-SVR with Non-PSD Kernels

Sequential minimal optimization (SMO) algorithm is effective in solving large-scale support vector machine (SVM). The existing algorithms all assume that the kernels are positive definite (PD) or positive semi-definite (PSD) and should meet the Mercer condition. Some kernels, however, such as sigmoid kernel, which originates from neural network and then is extensively used in SVM, are conditionally PD in certain circumstances; in addition, practically, it is often difficult to prove whether a kernel is PD or PSD or not except some well-known kernels. So, the applications of the existing algorithm of SMO are limited. Considering the deficiency of the traditional ones, this algorithm of solving ?-SVR with nonpositive semi-definite (non-PSD) kernels is proposed. Different from the existing algorithms which must consider four Lagrange multipliers, the algorithm proposed in this article just need to consider two Lagrange multipliers in the process of implementation. The proposed algorithm simplified the implementation by expanding the original dual programming of ?-SVR and solving its KKT conditions, thus being easily applied in solving ?-SVR with non-PSD kernels. The presented algorithm is evaluated using five benchmark problems and one reality problem. The results show that ?-SVR with non-PSD provides more accurate prediction than that with PD kernel.     

Weighted Support Vector Machine Using k-Means Clustering

The support vector machine (SVM) has been successfully applied to various classification areas with great flexibility and a high level of classification accuracy. However, the SVM is not suitable for the classification of large or imbalanced datasets because of significant computational problems and a classification bias toward the dominant class. The SVM combined with the k-means clustering (KM-SVM) is a fast algorithm developed to accelerate both the training and the prediction of SVM classifiers by using the cluster centers obtained from the k-means clustering. In the KM-SVM algorithm, however, the penalty of misclassification is treated equally for each cluster center even though the contributions of different cluster centers to the classification can be different. In order to improve classification accuracy, we propose the WKM–SVM algorithm which imposes different penalties for the misclassification of cluster centers by using the number of data points within each cluster as a weight. As an extension of the WKM–SVM, the recovery process based on WKM–SVM is suggested to incorporate the information near the optimal boundary. Furthermore, the proposed WKM–SVM can be successfully applied to imbalanced datasets with an appropriate weighting strategy. Experiments show the effectiveness of our proposed methods.     

Encoding dissimilarity data for statistical model building

We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A “newbie” algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a smoothing spline ANOVA penalized likelihood model, a support vector machine, or any model that will admit reproducing kernel Hilbert space components, for nonparametric regression, supervised learning, or semisupervised learning. Future work and open questions are discussed. The papers are: