基于同心超球面分割的支持向量预抽取方法

支持向量机仅仅由支持向量所决定,因此预先抽取支持向量参与训练是非常重要的。提出了一个基于同心超球面分割的支持向量预抽取方法,并在此基础上给出了HD-SVM训练算法。首先对样本的每一类分别用一些半径足够大的同心超球面进行分割,抽取出距离最优分类面较近的边界样本,这些样本最有可能成为支持向量;然后让边界样本作为初始工作集先参与训练。实验结果表明,该文的方法可以有效地对支持向量进行预抽取,避免了训练全部样本,使得训练速度明显得到提高。     

基于概率超球集神经网络的发动机故障诊断

提出了一种用于发动机故障检测与诊断的概率超球集神经网络．神经网络用概率集表示发动机故障模式,概率集是由超球聚集形成的集合体,超球是由球心和半径确定．概率超球集神经网络能在两次循环中完成学习过程,并能不断融合新样本信息和精炼已存在的故障模式．YF-20发动机故障检测与诊断的仿真研究验证了概率超球集神经网络分类器的优越性能．     

一种新的基于构造型RBF神经元网络分类算法

依据RBF神经元模型的几何解释,提出一种新的构造型神经网络分类算法.首先从样本数据本身入手,通过引入一个密度估计函数来对样本数据进行聚类分析;然后在特征空间里构造超球面,以逼近样本点分布的几何轮廓,从而将神经网络训练问题转化为点集＂包含＂问题.该算法有效克服了传统神经网络训练时间长、学习复杂的缺陷,同时也考虑了神经网络规模的优化问题.实验证明了该算法的有效性.     

一种新的RBF神经元网络分类算法

为了改善对人工神经网络行为的认识和研究中的"黑匣子"式的难以处理的状态,基于RBF神经元模型的几何解释,提出了一种新的RBF神经网络分类算法,算法把RBF神经元看作是高维空间里的超球面,从而将神经网络训练问题转化为点集"包含"问题.同传统的RBF网络相比,算法能够自动地优化RBF网络中核函数的个数、中心和宽度,同时,省去了传统RBF神经网络输出层线性连接权的计算,简化了网络的学习过程,大大缩短了训练时间,并且通过实验证明了算法的有效性.     

基于支持向量数据描述算法的SVM多分类新方法*

提出一种基于支持向量数据描述算法(SVDD)的多分类方法(S-MSVM).受SVDD的启发,该方法对每类样本建立一个超球来界定,但训练好的超球在所有情况下都是相交的.选择相交区域的样本单独建立超球,重复该步骤,直到相交区域消失或相交区域内没有样本点.给出了该方法的时间复杂度分析,并通过实验验证了该方法具有相对较好的训练精度.     

A Novel and Principled Multiclass Support Vector Machine

Support vector machines (SVMs) have been demonstrated very efficient for binary classification problems; however, computationally efficient and effective multiclass SVMs are still missing. Most existing multiclass SVM classifiers are constructed either by combining multiple binary SVM classifiers, which often perform moderately for some problems, or by converting multiclass problems into one single optimization problem, which is unfortunately computationally expensive. To address these issues, a novel and principled multiclass SVM based on geometric properties of hyperspheres, termed SVMGH, is proposed in this paper. Different from existing SVM‐based methods that seek a cutting hyperplane between two classes, SVMGH draws the discriminative information of each class by constructing a minimum hypersphere containing all class members, and then defines a label function based on the geometric properties of the minimum hyperspheres. We prove theoretically the geometric properties of the minimum hyperspheres to guarantee the validation of SVMGH. The computational efficiency is enhanced by a data reduction strategy as well as a fast training method. Experimental results demonstrate that the proposed SVMGH shows better performance and higher computational efficiency than the state of the art on multiclassification problems while maintaining comparable performance and efficiency on binary classification problems.     

基于模糊C均值聚类与单类支持向量机的音频隐写分析方法

针对传统的二分类音频隐写分析方法对未知隐写方法的适应性较差的问题,提出了一种基于模糊C均值(FCM)聚类与单类支持向量机(OC-SVM)的音频隐写分析方法。在训练过程中,首先对训练音频进行特征提取,包括短时傅里叶变换(STFT)频谱的统计特征和基于音频质量测度的特征,然后对所提取的特征进行FCM聚类得到C个聚类,最后送入多个超球面的OC-SVM分类器进行训练;检测过程中,对测试音频进行特征提取,根据多个超球面OC-SVM分类器的边界对待测音频进行检测。实验结果表明,该隐写分析方法对于几种典型的音频隐写方法能够较为正确地检测,满容量嵌入时,测试音频的总体检测率达到85.1%,与K-means聚类方法相比,所提方法的检测正确率提高了至少2%。该隐写分析方法比二分类的隐写分析方法更具有通用性,更适用于隐写方法事先未知情况下的隐写音频的检测。     

多超球面OC-SVM算法在隐秘图像检测中的应用

针对二类支持向量机分类器在图像密写分析应用中训练步骤复杂与推广性弱的缺点,把一类支持向量机（OC-SVM）引入算法,提出一种基于核的多超球面OC-SVM算法。算法利用核空间中样本特征差异突出的特性,首先对样本在核空间进行K-均值聚类,然后使用OC-SVMs对各子类训练建立多超球面分类模型,实现分类判决。实验结果表明,算法有效地实现了对隐秘图像的盲检测,提高了检测精度。     

Comments on ‘A simple sufficient condition for the stability of linear discrete time systems’

Berger (1982) has found the maximum hyperspheres in the stable domain of parameter space. We show that the maximum stable hyperspheres for odd-order systems contain some unstable polynomials, and propose a correction to Berger's results.     

超球体检测器覆盖问题的研究

研究表明实值否定选择算法在多维形状空间下呈现出很高的时间和空间复杂性。针对实值否定选择算法中最常采用的超球体检测器,在理论上研究了它的体积,以及体积随半径和维数变化的性质,以此分析了高复杂性出现的原因。针对检测器存在重叠的问题,基于蒙特卡罗方法提出了一个估计检测器覆盖率的算法,用于比较不同检测器生成算法。由于该算法基于随机分布和概率方法,它极大地简化了计算复杂性。     

广义最大间隔球形支持向量机

针对多类分类问题,提出一种超球支持向量机算法——广义最大间隔球形支持向量机,该算法利用两同心超球将正负类样本分隔开来,最大化两超球半径的差异,从而挖掘正负类样本的鉴别信息,同时对超球类支持向量机算法判决规则进行改进,引入模糊隶属度补充判决,弥补二类分类器投票决策的缺陷.理论分析了算法的相关性质,通过仿真实验验证了该算法的有效性.     

Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning

Despite the big success of transfer learning techniques in anomaly detection, it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification, especially for the data with a large distribution difference. To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper. First, by integrating a hypersphere adaptation constraint into...     

A new maximal-margin spherical-structured multi-class support vector machine

Support vector machines (SVMs), initially proposed for two-class classification problems, have been very successful in pattern recognition problems. For multi-class classification problems, the standard hyperplane-based SVMs are made by constructing and combining several maximal-margin hyperplanes, and each class of data is confined into a certain area constructed by those hyperplanes. Instead of using hyperplanes, hyperspheres that tightly enclosed the data of each class can be used. Since the class-specific hyperspheres are constructed for each class separately, the spherical-structured SVMs can be used to deal with the multi-class classification problem easily. In addition, the center and radius of the class-specific hypersphere characterize the distribution of examples from that class, and may be useful for dealing with imbalance problems. In this paper, we incorporate the concept of maximal margin into the spherical-structured SVMs. Besides, the proposed approach has the advantage of using a new parameter on controlling the number of support vectors. Experimental results show that the proposed method performs well on both artificial and benchmark datasets.     

De-identifying facial images using singular value decomposition and projections

In this paper, two methods are presented that manipulate images to hinder automatic face identification. They partly degrade image quality, so that humans can identify the persons in a scene, while face identification algorithms fail to do so. The approaches used involve: a) singular value decomposition (SVD) and b) image projections on hyperspheres. Simulation experiments verify that these methods reduce the percentage of correct face identification rate by over 90 %. Additionally, the final image is not degraded beyond recognition by humans, in contrast with the majority of other de-identification methods.     

基于清晰半径的模糊点二次聚类算法

针对模糊C-均值(FCM)聚类算法在模糊边界上容易出现划分错误的问题,提出一种对模糊点进行二次处理的改进算法。该算法以各类中的数据分布密度为依据,首先利用清晰点构成超球体中心区域,然后基于中心区域的清晰半径定义一种新的相似性距离,并利用该距离对模糊点的隶属度进行二次计算,重新确定其类别归属。实验结果显示,改进算法能有效纠正分类错误,提高模糊点的清晰度,在密度差异较大的数据集上具有一定的应用潜力。     

最大间隔最小体积球形支持向量机

结合支持向量机（ＳＶＭ）类间最大分类间隔和支持向量数据描述（ＳＶＤＤ）类内最小描述体积思想,提出一种新的学习机器模型———最大间隔最小体积球形支持向量机（ＭＭＨＳＶＭ）．模型建立两个大小不一的同心超球,将正负类样本分别映射到小超球内和大超球外,模型目标函数最大化两超球间隔,实现正负类类间间隔的最大化和各类类内体积的最小化,提高了模型的分类能力．理论分析和实验结果表明该算法是有效的．     

Training data development with the D-optimality criterion

The importance of using optimum experimental design (OED) concepts when selecting data for training a neural network is highlighted in this paper. We demonstrate that an optimality criterion borrowed from another field; namely the D-optimality criterion used in OED, can be used to enhance the training value of a small training data set. This is important in cases where resources are limited, and collecting data is expensive, hazardous, or time consuming. The analysis results in the cases considered indicate that even with a small set of training examples, so long as the training data set was chosen according to the D-optimality criterion, the network was able to generalize, and as a result, was able to fit complex surfaces     

The effect of training set size and composition on artificial neural network classification

Training set characteristics can have a significant effect on the performance of an image classification. In this paper the effect of variations in training set size and composition on the accuracy of classifications of synthetic and remotely sensed data sets by an artificial neural network and discriminant analysis are assessed. Attention is focused on the effects of variations in the overall size of the training set, in terms of the number of training samples, as well as on variations in the size of individual classes in the training set. The results showed that higher classification accuracies were generally derived from the artificial neural network, especially when small training sets only were available. It was also apparent that the opportunity of the artificial neural network to learn class appearance was influenced by the composition of the training set. The results indicated that the size of each class in the training set had an effect similar to. that of including a priori probabilities of class membership into the discriminant analysis. In the classification of the remotely sensed data set the classification accuracy was increased significantly as a result of increasing the number of training cases for abundant classes in the image.