支持向量回归中的预测信任度

Support vector machine (SVM)has been widely applied to classification and regression problems, but it suf-fers from some important limitations, one of the most significant being that it makes point predictions rather thangenerating probability output. A notion of predicting credibility is proposed in support vector regression machine based on the problem, which can make predicting value have a definite measure, and then relationship between pre-dicting credibility and noise is discussed. Finally, an example of predicting chaotic time series shows the rationality of the definition.     

Relationship Between Support Vector Set and Kernel Functions in SVM

Based on a constructive learning approach,covering algorithms,we investigate the relationship between support vector sets and kernel functions in support vector machines (SVM).An interesting result is obtained.That is,in the linearly non-separable case,any sample of a given sample set K can become a support vector under a certain kernel function.The result shows that when the sample set K is linearly non-separable,although the chosen kernel function satisfies Mercer‘s condition its corresponding support vector set is not necessarily the subset of K that plays a crucial role in classifying K.For a given sample set,what is the subset that plays the crucial role in classification?In order to explore the problem,a new concept,boundary or boundary points,is defined and its properties are discussed.Given a sample set K,we show that the decision functions for classifying the boundary points of K are the same as that for classifying the K itself.And the boundary points of K only depend on K and the structure of the space at which k is located and independent of the chosen approach for finding the boundary.Therefore,the boundary point set may become the subset of K that plays a crucial role in classification.These results are of importance to understand the principle of the support vector machine(SVM) and to develop new learning algorithms.     

Support Vector Machine and Artificial Neural Networks for Hydrological Cycles Classifications of a Water Reservoir in the Amazon

This paper presents a methodology based on computational intelligence techniques for classification of hydrological cycles that can infer the change in the physico-chemieal parameters and metals from the water of a reservoir in the Amazon. The methodology initially consists in perform a pre-processing the data to select the most relevant variables of the samples. After that, we compared two different machine learning classifiers, namely SVM （support vector machine） and ANN （artificial neural network）. The automatic model selection is made to choose the parameters of the classifiers. The results indicate that the support vector machine classifier using radial basis function or polynomial kernel exhibited superior results to ANN in terms of overall accuracy and robustness. The SVM classifier accuracy （89.1%） can be considered satisfactory, since there is a great variability of physico-chemical parameters and metals in the hydrological cycles and in the different ecosystems where are the sampling station.     

Geodesic Distance for Support Vector Machines

When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method.     

基于测地距离的支持向量机分类算法

When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method.     

Fault Diagnosis Based on MultiKernel Classification and Information Fusion Decision

In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories （sub-populations） containing observations （or instances） whose category membership is known. SVM （support vector machines） are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes fon~as the output, making it a non-probabilistic binary linear classifier. In pattern recognition problem, the selection of the features used for characterization an object to be classified is importance. Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, impticitly perform a nonlinear mapping 4~ of the input data in Rainto a high-dimensional feature space H. Cover＇s theorem states that if the transformation is nonlinear and the dimensionality of the feature space is high enough, then the input space may be transformed into a new feature space where the patterns are linearly separable with high probability.     

Toward Effective Knowledge Acquisition with First-Order Logic Induction

Knowledge acquisition with machine learning techniques is a fundamental requirement for knowledge discovery from databases and data mining systems.Two techniques in particular-inductive learning and theory revision-have been used toward this end.A method that combines both approaches to effectively acquire theories (regularity) from a set of training examples is presented.Inductive learning is used to acquire new regularity from the training examples;and theory revision is used to improve an initial theory.In addition,a theory preference criterion that is a combination of the MDL-based heuristic and the Laplace estimate has been successfully employed in the selection of the promising theory.The resulting algorithm developed by integrating inductive learning and theory revision and using the criterion has the ability to deal with complex problems,obtaining useful theories in terms of its predictive accuracy.     

A Decomposition and Coordination Scheduling Method for Flow-shop Problem Based on TOC

There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems, so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.     

基于约束理论的Flow-shop分解协调算法

There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems, so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.     

Credit scoring by feature-weighted support vector machines

Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.     

一种改进的模糊多类支持向量机算法

支持向量机是基于统计学习理论的新一代机器学习技术;由于使用结构风险最小化原则代替经验风险最小化原则,使它较好地解决了小样本情况下的学习问题;针对目前模糊支持向量机方法中,一般使用样本与类中心之间的距离关系构建隶属度函数的不足,以统计学习理论和支持向量机为基础,提出了一种改进的模糊多类支持向量机方法,它是在全局优化分类的基础上,引入模糊隶属函数,然后利用改进的序列最小最优化算法求解模糊多类支持向量机,实验结果显示运行时间减少了,方法是可行的和有效的.     

支持向量机及其在草图编辑手势识别中的应用

支持向量机(SVM)是一种基于统计学习理论的机器学习与模式识别方法。它通过结构风险最小化准则和核函数方法,较好地解决了小样本、非线性及高维模式识别问题。本文主要从联机手绘草图编辑的角度出发,谈谈支持向量机在草绘手势笔划识别中的具体应用。     

关于统计学习理论与支持向量机

模式识别、函数拟合及概率密度估计等都属于基于数据学习的问题,现有方法的重 要基础是传统的统计学,前提是有足够多样本,当样本数目有限时难以取得理想的效果.统计 学习理论(SLT)是由Vapnik等人提出的一种小样本统计理论,着重研究在小样本情况下的 统计规律及学习方法性质.SLT为机器学习问题建立了一个较好的理论框架,也发展了一种 新的通用学习算法--支持向量机(SVM),能够较好的解决小样本学习问题.目前,SLT和 SVM已成为国际上机器学习领域新的研究热点.本文是一篇综述,旨在介绍SLT和SVM的 基本思想、特点和研究发展现状,以引起国内学者的进一步关注.     

支持向量机在机械故障诊断中的应用研究

在机械故障诊断中,通常不具备有大量的故障样本,因此,制约了故障诊断技术向智能化方向发展。而基于统计学习理论(SLT)的支持向量机(SVM)方法正好克服了这方面的不足。统计学习理论是专门研究少样本情况下的统计规律及学习方法的理论。SLT理论和SVM方法为故障诊断技术向智能化发展提供了新的途径。该文讨论了支持向量机在故障诊断领域中应用的分类算法。并以滚动轴承的振动信号为例进行了试验论证。试验表明:SVM方法对具有少样本的故障诊断领域具有很强的适应性。     

基于支持向量机的机械故障智能分类研究

故障样本不足是制约故障诊断技术向智能化方向发展的主要原因之一，支持向量机（SVM)是一种基于统计学习理论(SLT)的机器学习算法，它能在训练样本很少的情况下达到很好的分类效果，从而为故障诊断技术向智能化发展提供了新的途径．本文介绍了支持向量机分类算法，以滚动轴承的故障分类为例，探讨了该算法在故障诊断领域中的应用，并与BP神经网络分类方法进行了对比研究，结果表明，SVM方法在少样本情况下的分类效果优于BP神经网络分类方法．     

支持向量机算法和软件ChemSVM介绍

Vladimir N.Vapnik等提出的统计学习理论（statistical learning theory，简称SLT）和支持向量机（support vector machine,简称SVM）算法已取得令人鼓舞的研究成果。本文旨在对这一新理论和新算法的原理作一介绍，并展望这一计算机学界的新成果在化学化工领域的应用前景，“ChemSVM”软件提供了通用的支持向量机算法，并将其与数据库，知识库，原子参数及其他数据挖掘方法有机地集成起来。     

基于SVM的综合评价方法研究

系统介绍了统计学习理论与支持向量机的基本思想，研究了它们在综合评价中的应用。分析了科研立项评审系统的设计方法，建立了基于SVM的评审系统。文末比较了新评审系统和采用其它方法如模糊排序、神经网络等建立的评审系统所分别取得的拟合效果，比较结果表明：采用支持向量机设计的评审系统结构简单、思路清晰地且能取得更为理想的评审结果。     

一种基于支持向量机的测井岩性预测新方法

支持向量机是基于统计学习理论的模式分类器，我们研究了一种基于SVM的测井岩性预测的新方法，通过这种方法我们完成了测井数据的分析．并预测出其对应的岩性、本文介绍了使用这种方法进行岩性预测的完整过程．并通过对实验结果的分析说明了这种方法的优点。     

基于核聚类方法的多层次支持向量机分类树

针对解决多类模式识别问题的SVM方法进行研究。在比较几种常用的多类SVM分类算法的基础上，提出一种基于核聚类方法的多层次SVM分类树，将核空问中的无监督学习方法和有监督学习方法结合起来，实现了一种结构更加简洁清晰、计算效率更高的多层SVM分类树算法，并在实验中取得了良好的结果．     

一种基于Vague-Sigmoid核的支持向量机研究

Sigmoid核最初起源于神经网络,目前在支持向量机中也得到了广泛应用,但由于核矩阵的非半正定性,其应用受到一些限制.研究表明Sigmoid核可以用简单的模糊三角隶属函数来近似替代,使得其学习效率能进一步提高.本文首先分析模糊支持向量机的特性,将模糊理论用于支持向量机的核中,并在此基础上提出了基于Vague-Sigmoid核函数的支持向量分类器.该方法充分结合了Vague集的自身优势,用基于Vague集的相似度量来代替了常规中的样本间的点积计算方法.将文中提出的方法应用于标准数据集中,并与传统的Sigmoid核方法、Fuzzy -Sigmoid核方法进行了实验分析, 实验表明文中提出的方法在不损失精度的情况下,能较好的提高算法的执行效率,取得了较好的实验结果;同时也表明在支持向量机中能利用Vague-S igmoid核取代替传统的Sigmoid核,从而减少对Sigmoid核的限制.