A one-layer recurrent neural network for support vector machine learning.

This paper presents a one-layer recurrent neural network for support vector machine (SVM) learning in pattern classification and regression. The SVM learning problem is first converted into an equivalent formulation, and then a one-layer recurrent neural network for SVM learning is proposed. The proposed neural network is guaranteed to obtain the optimal solution of support vector classification and regression. Compared with the existing two-layer neural network for the SVM classification, the proposed neural network has a low complexity for implementation. Moreover, the proposed neural network can converge exponentially to the optimal solution of SVM learning. The rate of the exponential convergence can be made arbitrarily high by simply turning up a scaling parameter. Simulation examples based on benchmark problems are discussed to show the good performance of the proposed neural network for SVM learning.     

基于SVM-MLP的乳腺癌预测

乳腺癌一直是影响女性健康最重要的问题之一,已经成为全球女性发病率最高的恶性肿瘤.近年来,利用机器学习和深度学习方法来诊断癌症已经成为发展较快的一个分支.通过使用逻辑回归模型(LR)、高斯核函数支持向量机(SVM)、前馈神经网络(MLP)对同一数据集进行预测,得出其中SVM迭代时间最短,前馈神经网络预测准确率最高.为了减...     

Unsupervised learning method for a support vector machine and its application to surface electromyogram recognition

The support vector machine (SVM) is known as one of the most influential and powerful tools for solving classification and regression problems, but the original SVM does not have an online learning technique. Therefore, many researchers have introduced online learning techniques to the SVM. In this article, we propose an unsupervised online learning method using a self-organized map for a SVM. Furthermore, the proposed method has a technique for the reconstruction of a SVM. We compare its performance with the original SVM, the supervised learning method for the SVM, and a neural network, and also test our proposed method on surface electromyogram recognition problems.     

A comparative study for content-based dynamic spam classification using four machine learning algorithms

The growth of email users has resulted in the dramatic increasing of the spam emails during the past few years. In this paper, four machine learning algorithms, which are Naïve Bayesian (NB), neural network (NN), support vector machine (SVM) and relevance vector machine (RVM), are proposed for spam classification. An empirical evaluation for them on the benchmark spam filtering corpora is presented. The experiments are performed based on different training set size and extracted feature size. Experimental results show that NN classifier is unsuitable for using alone as a spam rejection tool. Generally, the performances of SVM and RVM classifiers are obviously superior to NB classifier. Compared with SVM, RVM is shown to provide the similar classification result with less relevance vectors and much faster testing time. Despite the slower learning procedure, RVM is more suitable than SVM for spam classification in terms of the applications that require low complexity.     

基于深度学习的航空发动机传感器故障检测

针对传统反向传播(BP)神经网络和支持向量机(SVM)存在的过拟合、维数灾难、参数选择困难等问题,提出了一种基于深度学习算法的航空发动机传感器故障检测方法.对发动机参数记录仪采集的多维数据进行预处理,建立基于深度置信网络(DBN)的故障检测模型,利用预处理后的数据对检测模型进行训练,经过DBN故障检测模型逐层特征学习实现了传感器故障检测.仿真结果表明:在无人工特征提取和人工特征提取的情况下,基于DBN故障检测的准确率均高于BP神经网络和SVM模型.     

基于粒子群优化支持向量机的煤矿水位预测模型

支持向量机算法(SVM)具有可靠的全局最优性和良好的泛化能力,适用于有限样本的学习,而该算法的成功与否很大程度上取决于其参数的选择,而常规经验选取方法往往不能获得满意效果。利用粒子群算法(PSO)随机搜索策略对支持向量机参数进行优选,建立基于粒子群算法参数优化的支持向量机模型(PSO-SVM)。仿真结果表明,该优化模型比传统的人工神经网络(BP)模拟效果要好,在拟合精度方面有很大的提高,且具有较好的泛化能力。     

Support vector machine with adaptive parameters in financial time series forecasting

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.     

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．     

Online learning method using support vector machine for surface-electromyogram recognition

Research surface electromyogram (s-EMG) signal recognition using neural networks is a method which identifies the relation between s-EMG patterns. However, it is not sufficiently satisfying for the user because s-EMG signals change according to muscle wasting or to changes in the electrode position, etc. A support vector machine (SVM) is one of the most powerful tools for solving classification problems, but it does not have an online learning technique. In this article, we propose an online learning method using SVM with a pairwise coupling technique for s-EMG recognition. We compared its performance with the original SVM and a neural network. Simulation results showed that our proposed method is better than the original SVM. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming.

In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.     

Deep kernel learning in core vector machines

In machine learning literature, deep learning methods have been moving toward greater heights by giving due importance in both data representation and classification methods. The recently developed multilayered arc-cosine kernel leverages the possibilities of extending deep learning features into the kernel machines. Even though this kernel has been widely used in conjunction with support vector machines (SVM) on small-size datasets, it does not seem to be a feasible solution for the modern real-world applications that involve very large size datasets. There are lot of avenues where the scalability aspects of deep kernel machines in handling large dataset need to be evaluated. In machine learning literature, core vector machine (CVM) is being used as a scaling up mechanism for traditional SVMs. In CVM, the quadratic programming problem involved in SVM is reformulated as an equivalent minimum enclosing ball problem and then solved by using a subset of training sample (Core Set) obtained by a faster \((1+\epsilon )\) approximation algorithm. This paper explores the possibilities of using principles of core vector machines as a scaling up mechanism for deep support vector machine with arc-cosine kernel. Experiments on different datasets show that the proposed system gives high classification accuracy with reasonable training time compared to traditional core vector machines, deep support vector machines with arc-cosine kernel and deep convolutional neural network.     

Drug design by machine learning: support vector machines for pharmaceutical data analysis.

We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.     

支持向量机与RBF神经网络回归性能比较研究

支持向量机与RBF神经网络相比各有优缺点,通过对支持向量机与RBF神经网络的研究,从理论上分析了这两种学习机在回归预测原理上的异同,通过仿真实验对比了两者在测试集上的逼近能力及泛化能力。仿真结果表明,对于小样本集,支持向量机的逼近能力及泛化能力要优于RBF神经网络。对实际应用中回归模型的选择问题提出了建议。     

Drug design by machine learning: support vector machines for pharmaceutical data analysis

We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure–activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.     

A Self-Organizing TS-Type Fuzzy Network With Support Vector Learning and its Application to Classification Problems

A self-organizing Takagi-Sugeno (TS)-type fuzzy network with support vector learning (SOTFN-SV) is proposed in this paper. The proposed SOTFN-SV is inspired by analysis of TS-type fuzzy systems and composite-kernel support vector machine (SVM). SOTFN-SV is a fuzzy system constructed by the hybridization of fuzzy clustering and SVM. The antecedent part of SOTFN-SV is generated via fuzzy clustering of the input data, and then SVM is used to tune the consequent part parameters to give the network better generalization performance. For demonstration, SOTFN-SV is applied to several classification problems, especially the skin color classification problem. In the skin color classification application, each color pixel is represented by hue and saturation (HS) color space. To represent color information by histogram as accurately as possible, a nonuniform partition of HS space is proposed. For comparison, SVMs and other fuzzy systems trained by SVM or neural networks are applied to the same classification problems. The advantages of SOTFN-SV are verified by comparisons with the results of these methods.     

基于支持向量回归的唇动参数预测

支持向量机学习方法以结构风险最小化原则取代传统机器学习方法中的经验风险最小化原则，在有限样本的机器学习中显示出优异的性能．将这一新的统计学习方法应用到多媒体交互作用的研究中，用支持向量回归的方法由语音预测唇动参数．通过对语音的线性预测系数进行主分量分析，有效地压缩了声学特征参数的维数．结合交叉校验和最速下降优化方法，选择最佳的支持向量回归学习参数．在汉语0～9的任意数字串上对唇高参数的预测实验结果达到了均方误差0．0096，平均幅度误差7．2％及相关系数0．8的效果．这一结果优于一个文中优化过的人工神经网络所达到的性能，说明这一方法很有潜力．     

Fault diagnosis of power transformer based on support vector machine with genetic algorithm

Diagnosis of potential faults concealed inside power transformers is the key of ensuring stable electrical power supply to consumers. Support vector machine (SVM) is a new machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The selection of SVM parameters has an important influence on the classification accuracy of SVM. However, it is very difficult to select appropriate SVM parameters. In this study, support vector machine with genetic algorithm (SVMG) is applied to fault diagnosis of a power transformer, in which genetic algorithm (GA) is used to select appropriate free parameters of SVM. The experimental data from several electric power companies in China are used to illustrate the performance of the proposed SVMG model. The experimental results indicate that the SVMG method can achieve higher diagnostic accuracy than IEC three ratios, normal SVM classifier and artificial neural network.     

Hyperspectral data classification based on flexible momentum deep convolution neural network

Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous effort has been concentrate on the classification problem. However, most of the methods accuracy is not high enough due to the fact that they do not extract features in a deep manner. In this paper, a new hyperspectral data classification skeleton based on exponential flexible momentum deep convolution neural network (EFM-CNN) is proposed. First, the fitness of convolution neural network is substantiated by following classical spectral information-based classification. Then, a novel deep architecture is proposed, which is a hybrid of principle component analysis (PCA), improved convolution neural network based on exponential flexible momentum and support vector machine (SVM). Experimental results indicate that the classifier can effectively improve the accuracy with the state-of-the-art algorithms. And compared with homologous parameters momentum updating methods such as adaptive momentum method, standard momentum gradient method and elastic momentum method, on LeNet5 net and multiple neural network, the accuracy obtained of proposed algorithm increases by 2.6% and 6.5% on average respectively.

     

面向色织生产过程的整经轴数智能预测算法

整经轴数是色织生产中由整经工艺确定的一个品种内待加工的整经轴数目．该参数是制定色织生产作业计划所需的重要特征量。由于整经工艺制定过程非常复杂,其通常需要较长时间才能完成,因而该参数在调度时难以有效获取。为解决复杂色织生产过程调度所需的整经轴数预测问题,提出了一种智能预测算法。该算法针对实际问题特点,将支持向量机与神经网络技术相结合,由支持向量机对与整经轴数相关的参数进行特征提取．并将特征提取后的特征属性作为神经网络的输入,相应的特征属性权重被用以指导神经网络的学习过程。数值计算及实际制造企业应用效果表明该算法是有效的,能满足面向实际色织生产过程的整经轴数预测需要。     

Credit rating analysis with support vector machines and neural networks: a market comparative study

Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.