The Relaxed Online Maximum Margin Algorithm

We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen examples correctly with the maximum margin. It is known that such a maximum-margin hypothesis can be computed by minimizing the length of the weight vector subject to a number of linear constraints. ROMMA works by maintaining a relatively simple relaxation of these constraints that can be efficiently updated. We prove a mistake bound for ROMMA that is the same as that proved for the perceptron algorithm. Our analysis implies that the maximum-margin algorithm also satisfies this mistake bound; this is the first worst-case performance guarantee for this algorithm. We describe some experiments using ROMMA and a variant that updates its hypothesis more aggressively as batch algorithms to recognize handwritten digits. The computational complexity and simplicity of these algorithms is similar to that of perceptron algorithm, but their generalization is much better. We show that a batch algorithm based on aggressive ROMMA converges to the fixed threshold SVM hypothesis.     

Error Concealment Using Adaptive Multilayer Perceptrons (MLPs) for Block-Based Image Coding

Image coding algorithms such as Vector Quantisation (VQ), JPEG and MPEG have been widely used for encoding image and video. These compression systems utilise block-based coding techniques to achieve a higher compression ratio. However, a cell loss or a random bit error during network transmission will permeate into the whole block, and then generate several damaged blocks. Therefore, an efficient Error Concealment (EC) scheme is essential for diminishing the impact of damaged blocks in a compressed image. In this paper, a novel adaptive EC algorithm is proposed to conceal the error for block-based image coding systems by using neural network techniques in the spatial domain. In the proposed algorithm, only the intra-frame information is used for reconstructing the image with damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the PSNR evaluation of a reconstructed image are significantly improved using the proposed EC algorithm.     

基于最小错误率的SAR图象分割方法研究

文章提出了一种基于最小错误率的SAR图象自动分割算法。对直方图呈现出多模分布特征的SAR图象,首先运用有限高斯混合分布对SAR图象特征空间的数据统计模型进行估计;其次基于最小错误率原理选取SAR图象自动分割阈值,在先验概率未知和估计条件下,获得目标及其阴影区域的检测结果;最后对两种图象分割结果进行了分析并与SAR图象目标检测的经典方法-恒虚警(CFAR)目标检测方法作了比较。仿真结果表明在先验概率估计下的图象自动分割具有明显的优势和较大的应用潜力。     

Concave-Convex Programming for Ramp Loss-Based Maximum Margin and Minimum Volume Twin Spheres Machine

Neural Processing Letters - Twin hyper-sphere support vector machine (THSVM) classifies two classes of samples via two hyper-spheres instead of a pair of nonparallel hyper-planes in the...     

最小费用最大流问题的一种新算法

现有的最小费用最大流算法都有自身的缺陷,增广链的选取不当会给计算带来不便,同时费用也达不到理想的效果。鉴于对最小费用最大流算法的增广链选取和最小费用的探索,文章通过对费用差的定义给出了一种求最小费用最大流的新算法。新算法的原则是优先选择费用差最小的有向路径进行增广,当费用差相同时就选择修正后的路径。通过对最小费用最大流算法的改进,新算法易理解且便于计算。通过实例说明了新算法的有效性和执行效率。     

WCDMA系统中的差分最小均方误差多用户检测算法

宽带码分多址(Wideband code division multiple access,WCDMA)系统中现有的自适应多用户检测算法需要训练比特,不能适应快衰落信道。本文根据WCDMA系统中特有的二级扩频体制的特点,提出基于差分最小均方误差(Differential minimum mean square error,DMMSE)准则的自适应多用户检测算法。该算法根据相邻接收符号幅度变化的比率变化自适应调整横向滤波器的权系数,从而不需要通过训练比特开销跟踪信道状态信息的方式来抑制多址干扰。仿真结果表明,在存在多普勒频移的传播环境下,DMMSE算法的误码性能优于现有的自适应最小均方误差(Minimum mean square error,MMSE)算法。     

Empirical Evidence for Ultrametric Structure in Multi-layer Perceptron Error Surfaces

Combinatorial optimization problems share an interesting property with spin glass systems in that their state spaces can exhibit ultrametric structure. We use sampling methods to analyse the error surfaces of feedforward multi-layer perceptron neural networks learning encoder problems. The third order statistics of these points of attraction are examined and found to be arranged in a highly ultrametric way. This is a unique result for a finite, continuous parameter space. The implications of this result are discussed.     

Maximum Margin Multisurface Support Tensor Machines with application to image classification and segmentation

Virtually all previous classifier models take vectors as inputs, performing directly based on the vector patterns. But it is highly necessary to consider images as matrices in real applications. In this paper, we represent images as second order tensors or matrices. We then propose two novel tensor algorithms, which are referred to as Maximum Margin Multisurface Proximal Support Tensor Machine (M³PSTM) and Maximum Margin Multi-weight Vector Projection Support Tensor Machine (M³VSTM), for classifying and segmenting the images. M³PSTM and M³VSTM operate in tensor space and aim at computing two proximal tensor planes for multisurface learning. To avoid the singularity problem, maximum margin criterion is used for formulating the optimization problems. Thus the proposed tensor classifiers have an analytic form of projection axes and can achieve the maximum margin representations for classification. With tensor representation, the number of estimated parameters is significantly reduced, which makes M³PSTM and M³VSTM more computationally efficient when handing the high-dimensional datasets than applying the vector representations based methods. Thorough image classification and segmentation simulations on the benchmark UCI and real datasets verify the efficiency and validity of our approaches. The visual and numerical results show M³PSTM and M³VSTM deliver comparable or even better performance than some state-of-the-art classification algorithms.     

混合控制系统的稳定裕度分析与计算

数字控制与连续对象组成的混合系统是常见的控制形式,而通过有效的计算方法得到混合系统的稳定裕度是衡量控制系统性能指标的重要工作。通过一个具体的数字实例,对于目前常用的几种混合系统的稳定裕度计算方法进行了比较,明确了正确的计算步骤和方法,即只有对于整个开环传递函数进行整体ZOH离散化才能够得到真实结果,并指出通过Matlab中的相应函数可以得到精确的数值解。最后给出了混合系统设计的指导性意见。     

基于Youla参数化的动态随机系统最小误差熵控制

针对具有随机干扰的动态系统,提出一种最小误差熵控制方法。基本思想是应用Youla参数化公式构建具有闭环稳定性的反馈控制策略。其中Renyis熵被作为跟踪误差信息以测度闭环系统的不确定性,Youla参数被优化以使闭环系统误差熵最小,且一个仿真实例也表明了所提算法的有效性。     

打磨机器人误差建模与参数辨识

简要介绍了一种实用的机器人位姿误差建模方法一摄动法,并利用此方法建立了打磨机器人位姿误差模型,对利用三坐标测量机测量的误差数据进行了建模补偿。误差参数辨识中,在传统的九线法的基础上,提出了单点法测量机器人的误差参数,克服了九线法中三测量点位置误差对测量结果的影响,提高了测量结果的可信度。实验结果表明,建模补偿后机器人位姿误差平均值降低到初始值的1／5左右,最大值降低到初始值的1／6左右,验证了所建模型的正确性和参数辨识方法的有效性。     

用于非高斯系统降维的最小残差熵主元网络

基于最小均方误差的主元分析和主元神经网络是有效的多变量降维统计技术,它们所提取的主元含有系统最大方差.非高斯随机系统的近似模型应当含有系统最大信息熵,但包含最大方差并不一定包含最大信息熵.该文提出一种以最小残差熵为通用指标的非线性主元神经网络模型,并给出了一种基于Parzen窗口密度函数估计的熵近似计算方法和网络学习算法.然后从信息论角度分析了,在高斯随机系统中基于最小残差熵和最小均方差为指标的主元网络学习结果具有一致性.最后以仿真验证该方法的有效性,并与基于最小均方误差的主元分析和主元神经网络方法的计算结果进行对比性分析.     

Error Estimation and Control for ODEs

This article is about the numerical solution of initial value problems for systems of ordinary differential equations. At first these problems were solved with a fixed method and constant step size, but nowadays the general-purpose codes vary the step size, and possibly the method, as the integration proceeds. Estimating and controlling some measure of error by variation of step size/method inspires some confidence in the numerical solution and makes possible the solution of hard problems. Common ways of doing this are explained briefly in the article     

Error estimation and control for ODEs

This article is about the numerical solution of initial value problems for systems of ordinary differential equations. At first these problems were solved with a fixed method and constant step size, but nowadays the general-purpose codes vary the step size, and possibly the method, as the integration proceeds. Estimating and controlling some measure of error by variation of step size/method inspires some confidence in the numerical solution and makes possible the solution of hard problems. Common ways of doing this are explained briefly in the article.     

Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit

In this paper, we review an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function. We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas we have previously (Lai et al., 2000; Fyfe and MacDonald, 2002) viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit. We illustrate this on both artificial and real data sets.     

基于错误传播分析的软件脆弱点识别方法研究

在太空环境中,软件系统经常受到各种辐射现象的影响.在此类环境下,寻找软件脆弱点主要是考虑环境扰动对该软件的影响.文中提出了一种由环境扰动引入的软件脆弱点的分析方法.首先在对软件系统模块化的基础上,通过在两个层面上分析错误在软件中的生成及其传播过程,给出寻找软件脆弱点的理论框架,随后进一步给出该框架中一些参数的实验估计方法,最后给出该框架在某卫星光纤陀螺捷联航姿控制系统上的应用.应用结果令人满意.     

基于端到端质量优化的无线视频传输错误控制

帧内更新是在视频编码中提高视频应用容错能力的一种常见方法。过多的帧内编码宏块会降低编码的效率。在实时视频传输系统中,为了能获得最佳的端到端的视频质量,在容错能力和编码效率之间取得平衡是一个很重要的因素。基于对视频质量影响其他因素(视频压缩的量化步长,信道条件等)的分析,本文提出一种端到端跨层优化机制,通过联合自适应优化视频编码的帧内更新比例,编码的量化参数和物理层的自适应调制编码(AMC,adaptive modulation and coding)模式最小化接收端的视频失真。仿真结果表明,优化后视频的抗错误能力得到明显提高,在不同信道条件下的视频峰值信噪比(PSNR)能够提高0.5到7dB。     

决策树C4.5算法在天然气输差分析中的应用

分类器是数据挖掘的一种基本方法。文章首先介绍了分类器的基本概念和决策树构建思路,然后讲述了在天然气信息数据库的基础上如何建立决策树分类器(用Java语言)和寻找影响输差较大的因素,从而得出一些实用的预测输差的规则。     

视频通信中的抗误码技术

随着第三代移动通信和宽带无线技术的发展,视频抗误码与纠错技术是近年来多媒体通信领域研究的热点之一。该文从视频通信的系统模型出发,对视频通信中抗误码方法的研究及新进展进行了全面的总结。分别对信源编码端的抗误码、信道的差错控制技术、信源信道联合编码、解码端误码检测和误码隐藏方法等进行了详细评述,并总结了国际编码标准H.263+、H.263++、MPEG-4、H.264中的抗误码技术的发展,同时,指出了视频抗误码技术的发展方向和研究重点。