一种新型的多元分类支持向量机

最小二乘支持向量机采用最小二乘线性系统代替传统的支持向量机采用二次规划方法解决模式识别问题。该文详细推理和分析了二元分类最小二乘支持向量机算法，构建了多元分类最小二乘支持向量机，并通过典型样本进行测试，结果表明采用多元分类最小二乘支持向量机进行模式识别是有效、可行的。     

Hybrid Generative-Discriminative Visual Categorization

Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful features, one of which is the ability to naturally establish explicit correspondence between model components and scene features—this, in turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative approach, using ‘Fisher Kernels’ (Jaakola, T., et al. in Advances in neural information processing systems, Vol. 11, pp. 487–493, 1999), which retains most of the desirable properties of generative methods, while increasing the classification performance through a discriminative setting. Our experiments, conducted on a number of popular benchmarks, show strong performance improvements over the corresponding generative approach. In addition, we demonstrate how this hybrid learning paradigm can be extended to address several outstanding challenges within computer vision including how to combine multiple object models and learning with unlabeled data.     

基于新型机器学习的电子装备系统智能故障诊断研究

支持向量机是一种基于结构风险最小原则的新型机器学习方法,具有完备的理论依据和良好的学习泛化能力。该文针对电子装备系统特征,采用支持向量机算法构建智能故障诊断模型,并对典型电子设备进行故障诊断。结果表明,该诊断模型是可行的、有效的,具有一定工程应用价值。     

支持向量机

1 前言基于数据的机器学习是人工智能技术中的重要方面,从观测数据(样本)出发寻找数据中的模式和数据间的函数依赖规律,利用这些模式和函数依赖对未来数据或无法观测的数据进行分类、识别和预测。关于其实现方法大致可以分为三种,第一种是经典的(参数)统计估计方法,在这种方法中,参数的相关形式是已知的,训练样本用来估计参数的值。这种方法有很大的局限性,首先,它需要已知样本分布形式,其次传统统计学研究的是样本数目趋于无穷大时的渐近理论,现有学习方法也多是基于此假设,但在实际问题中,样本数往往是有限的,因此一些理论上很优秀的学习方法实际中表现却可     

Color image watermark extraction based on support vector machines

This work proposes a novel watermarking technique called SVM-based Color Image Watermarking (SCIW), based on support vector machines (SVMs) for the authentication of color images. To protect the copyright of a color image, a signature (a watermark), which is represented by a sequence of binary data, is embedded in the color image. The watermark-extraction issue can be treated as a classification problem involving binary classes. The SCIW method constructs a set of training patterns with the use of binary labels by employing three image features, which are the differences between a local image statistic and the luminance value of the center pixel in a sliding window with three distinct shapes. This set of training patterns is gathered from a pair of images, an original image and its corresponding watermarked image in the spatial domain. A quasi-optimal hyperplane (a binary classifier) can be realized by an SVM. The SCIW method utilizes this set of training patterns to train the SVM and then applies the trained SVM to classify a set of testing patterns. Following the results produced by the classifier (the trained SVM), the SCIW method retrieves the hidden signature without the original image during watermark extraction. Experimental results have demonstrated that the SCIW method is sufficiently robust against several color-image manipulations, and that it outperforms other proposed methods considered in this work.     

最小二乘支持向量机算法研究

1 引言支持向量机(SVM,Support Vector Machines)是基于结构风险最小化的统计学习方法,它具有完备的统计学习理论基础和出色的学习性能,在模式识别和函数估计中得到了有效的应用(Vapnik,1995,1998)。支持向量机方法一方面通过把数据映射到高维空间,解决原始空间中数据线性不可分问题;另一方面,通过构造最优分类超平面进行数据分类。神经网络通过基于梯度迭代的方法进行数据学习,容易陷入局部最小值,支持向量机是通过解决一个二次规划问题,来获得     

支持向量机处理大规模问题算法综述

支持向量机在处理大规模问题时存在训练时间过长和内存空间需求过大的问题.分析了支持向量机在处理大规模问题时存在的局限性;对利用支持向量机处理大规模问题的各种算法进行了分类,并对每种算法的研究状况进行了较全面而深入的综述;对该领域内值得进一步研究的问题进行了讨论.     

一种加权支持向量机分类算法

提出了一种加权C—SVM分类算法,并从理论上分析了算法的性能。该算法通过引入类权重因子和样本权重因子实现了类加权和样本加权两种功能。实验结果表明,该算法可以有效地解决由类大小不均衡引发的分类错误问题以及重要样本的错分问题。     

新型SVM对时间序列预测研究

In this paper, we present a new support vector machines-least squares support vector machines (LS-SVMs). While standard SVMs solutions involve solving quadratic or linear programming problems, the least squaresversion of SVMs corresponds to solving a set of linear equations, due to equality instead of inequality constraints in the problem formulation. In LS-SVMs, Mercer condition is still applicable. Hence several type of kernels such aspolynomial, RBF's and MLP's can be used. Here we use LS-SVMs to time series prediction compared to radial basisfunction neural networks. We consider a noisy (Gaussian and uniform noise)Mackey-Glass time series. The resultsshow that least squares support vector machines is excellent for time series prediction even with high noise.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

基于多人决策理论的船舶辐射噪声识别

船舶辐射噪声的识别非常复杂,为提高其分类器的分类性能和可靠性．提出一种基于多人决策理论的多分类器决策模型和算法．通过采用Welch谱、线性预测编码谱和Burg谱3种特征对应的BP神经网络和支持向量机分类器组成的6个分类结果进行群体决策,并对海上实测的3类目标辐射噪声数据进行分类．实验结果表明,对3类目标的总体正确识别概率达到96．29％．比单个分类器具有更好的分类性能．     

Comprehensive evaluation of twin SVM based classifiers on UCI datasets

In the past decade, twin support vector machine (TWSVM) based classifiers have received considerable attention from the research community. In this paper, we analyze the performance of 8 variants of TWSVM based classifiers along with 179 classifiers evaluated in Fernandez-Delgado et al. (2014) from 17 different families on 90 University of California Irvine (UCI) benchmark datasets from various domains. Results of these classifiers are exhaustively analyzed using various performance criteria. Statistical testing is performed using Friedman Rank (FRank). Our experiments show that two least square TWSVM based classifiers (ILSTSVM_m, and RELS-TSVM_m) are the top two ranked methods among 187 classifiers and they significantly outperform all other classifiers according to Friedman Rank. Overall, this paper bridges the evaluational benchmarking gap between various TWSVM variants and the classifiers from other families. Codes of this paper are provided on authors’ homepages to reproduce the presented results and figures in this paper.     

Cycle-breaking acceleration for support vector regression

Support vector regression (SVR) is a powerful tool in modeling and prediction tasks with widespread application in many areas. The most representative algorithms to train SVR models are Shevade et al.'s Modification 2 and Lin's WSS1 and WSS2 methods in the LIBSVM library. Both are variants of standard SMO in which the updating pairs selected are those that most violate the Karush-Kuhn-Tucker optimality conditions, to which LIBSVM adds a heuristic to improve the decrease in the objective function. In this paper, and after presenting a simple derivation of the updating procedure based on a greedy maximization of the gain in the objective function, we show how cycle-breaking techniques that accelerate the convergence of support vector machines (SVM) in classification can also be applied under this framework, resulting in significantly improved training times for SVR.     

深空探测航天器姿态控制系统故障定位

执行机构与敏感器故障检测与定位是深空探测任务卫星平台可靠运行的前提和保障.本文从数据的角度出发,结合姿控系统工作机理,提出一种基于神经网络和支持向量机结合的故障诊断方法用于检测并定位故障.故障诊断方法分为3步,首先采集姿控系统的状态信息,采用神经网络对闭环姿控系统中未知动态特性建模并进行预测;然后将姿控系统敏感器信号与神经网络预测输出比较生成残差并提取故障特征;最后采用支持向量机辨识残差特征检测故障,并结合运动学特性分析定位故障.仿真结果表明本文所提方法可以有效提取、辨识故障特征,实现执行器与敏感器的故障检测定位.     

SVOIS: Support Vector Oriented Instance Selection for text classification

Automatic text classification is usually based on models constructed through learning from training examples. However, as the size of text document repositories grows rapidly, the storage requirements and computational cost of model learning is becoming ever higher. Instance selection is one solution to overcoming this limitation. The aim is to reduce the amount of data by filtering out noisy data from a given training dataset. A number of instance selection algorithms have been proposed in the literature, such as ENN, IB3, ICF, and DROP3. However, all of these methods have been developed for the k-nearest neighbor (k-NN) classifier. In addition, their performance has not been examined over the text classification domain where the dimensionality of the dataset is usually very high. The support vector machines (SVM) are core text classification techniques. In this study, a novel instance selection method, called Support Vector Oriented Instance Selection (SVOIS), is proposed. First of all, a regression plane in the original feature space is identified by utilizing a threshold distance between the given training instances and their class centers. Then, another threshold distance, between the identified data (forming the regression plane) and the regression plane, is used to decide on the support vectors for the selected instances. The experimental results based on the TechTC-100 dataset show the superior performance of SVOIS over other state-of-the-art algorithms. In particular, using SVOIS to select text documents allows the k-NN and SVM classifiers perform better than without instance selection.     

Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives

In this paper, we propose a regularized least squares approach based support vector machine for simultaneously approximating a function and its derivatives. The proposed algorithm is simple and fast as no quadratic programming solver needs to be employed. Effectively, only the solution of a structured system of linear equations is needed.     

An ensemble of support vector machines for predicting virulent proteins

It is important to develop a reliable system for predicting bacterial virulent proteins for finding novel drug/vaccine and for understanding virulence mechanisms in pathogens.In this work we have proposed a bacterial virulent protein prediction method based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence of a given protein. It is well known in the literature that the features extracted from the evolutionary information of a given protein are better than the features extracted from the amino acid sequence. Our method tries to fill the gap between the amino acid sequence based approaches and the evolutionary information based approaches.An extensive evaluation according to a blind testing protocol, where the parameters of the system are calculated using the training set and the system is validated in three different independent datasets, has demonstrated the validity of the proposed method.     

A novel excitation controller using support vector machines and approximate models

This paper proposes a novel excitation controller using support vector machines （SVM） and approximate models. The nonlinear control law is derived directly based on an input-output approximation method via Taylor expansion, which not only avoids complex control development and intensive computation, but also avoids online learning or adjustment. Only a general SVM modelling technique is involved in both model identification and controller implementation. The robustness of the stability is rigorously established using the Lyapunov method. Several simulations demonstrate the effectiveness of the proposed excitation controller.     

Multi-class pattern classification using neural networks

Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and P-against-Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.