灵敏度正则化极限学习机及其在数字识别中的应用

针对正则化极限学习机（RELM）中隐节点数影响分类准确性问题,提出一种灵敏度正则化极限学习机（SRELM）算法.首先根据隐含层激活函数的输出及其相对应的输出层权重系数,推导实际值与隐节点输出值残差相对于隐节点的灵敏度计算公式,然后根据不同隐节点的灵敏度进行排序,利用优化样本的分类准确率删减次要隐节点,从而有效提高SRELM的分类准确率.MNIST手写体数字库实验结果表明,相比于传统的SVM和RELM, SRELM方法的耗时与RELM相差不大,均明显低于SVM, SRELM对手写数字的识别准确率最高.     

基于RELM的时间序列数据加权集成分类方法

时间序列数据通常是指一系列带有时间间隔的实值型数据,广泛存在于煤矿、金融和医疗等领域。为解决现有时间序列数据分类问题中存在的含有大量噪声、预测精度低和泛化性能差的问题,提出了一种基于正则化极限学习机(RELM)的时间序列数据加权集成分类方法。首先,针对时间序列数据中所含有的噪声,利用小波包变换方法对时间序列数据进行去噪处理。其次,针对时间序列数据分类方法预测精度低、泛化性能较差的问题,提出了一种基于RELM的加权集成分类方法。该方法通过训练正则化极限学习机(RELM)隐藏层节点数量的方法,有效选取RELM基分类器;通过粒子群优化(PSO)算法,对RELM基分类器的权值进行优化;实现对时间序列数据的加权集成分类。实验结果表明,该分类方法能够对时间序列数据进行有效分类,并提升了分类精度。     

基于极限学习机的无监督领域适应分类器

在构建基于极限学习机的无监督自适应分类器时, 隐含层的参数通常都是随机选取的, 而随机选取的参数不具备领域适应能力. 为了增强跨领域极限学习机的知识迁移能力,提出一种新的基于极限学习机的无监督领域适应分类器学习方法, 该方法主要利用自编码极限学习机对源域和目标域数据进行重构学习, 从而可以获得具有领域不变特性的隐含层参数. 进一步, 结合联合概率分布匹配和流形正则的思想, 对输出层权重进行自适应调整. 所提出算法能对极限学习机的两层参数均赋予领域适应能力,在字符数据集和对象识别数据集上的实验结果表明其具有较高的跨领域分类精度.     

集成重复训练极限学习机的数据分类

极限学习机是一种随机化算法,它随机生成单隐含层神经网络输入层连接权和隐含层偏置,用分析的方法确定输出层连接权。给定网络结构,用极限学习机重复训练网络,会得到不同的学习模型。本文提出了一种集成模型对数据进行分类的方法。首先用极限学习机算法重复训练若干个单隐含层前馈神经网络,然后用多数投票法集成训练好的神经网络,最后用集成模型对数据进行分类,并在10个数据集上和极限学习机及集成极限学习机进行了实验比较。实验结果表明,本文提出的方法优于极限学习机和集成极限学习机。     

ELM优化的深度自编码分类算法

针对自编码神经网络训练时间长的问题,提出了一种改进的深度自编码神经网络算法。首先利用极限学习机(extreme learning machine,ELM)作为自编码块,构建多层自编码神经网络,以提高分类准确率。采用ELM能避免大量的迭代过程,减少网络训练时间。其次为实现分类,在各输出层中加入标签节点,对实际输出与各样本的期望标签进行比对,使原始的自编码无监督学习转化为监督学习过程,从而在深度学习的过程中实现分类训练。为验证该方法的有效性,在多个UCI数据集中进行广泛的测试。实验结果表明,与其他自编码网络和RBF(radial basis function)神经网络相比,该方法取得了良好的分类准确率,并且有效提高了训练速度。     

基于鱼群优化算法和Cholesky分解的RELM的基因表达数据分类

提出一种基于鱼群优化算法和Cholesky分解的改进的正则极限学习机算法(FSC-RELM)来对基因表达数据进行分类。FSC-RELM算法中,首先用鱼群优化算法对RELM输入层权值进行优化,其中目标函数定义为误差函数的倒数;再对RELM输出层权值矩阵进行分解,采用Cholesky分解法进行优化,以提高算法速度,减少训练时间。为了评价算法性能,对若干标准基因数据集进行了实验,结果表明,FSC-RELM算法在较短的时间内可以获得较高的分类精度,性能优异。     

基于径向基神经网络和正则化极限学习机的多标签学习模型*

相比径向基(RBF)神经网络,极限学习机(ELM)训练速度更快,泛化能力更强.同时,近邻传播聚类算法(AP)可以自动确定聚类个数.因此,文中提出融合AP聚类、多标签RBF(ML-RBF)和正则化ELM(RELM)的多标签学习模型(ML-AP-RBF-RELM).首先,在该模型中输入层使用ML-RBF进行映射,且通过AP聚类算法自动确定每一类标签的聚类个数,计算隐层节点个数.然后,利用每类标签的聚类个数通过K均值聚类确定隐层节点RBF函数的中心.最后,通过RELM快速求解隐层到输出层的连接权值.实验表明,ML-AP-RBF-RELM效果较好.     

基于SDAE与RELM的EEG情感识别方法

针对情感识别中堆叠式自动编码器存在反向传播方法收敛速度慢和容易陷入局部最优的问题,提出一种基于堆叠式降噪自动编码器（SDAE）和正则化极限学习机（RELM）的情感状态识别方法。从脑电信号的时域、频域和时频域中提取表征情感状态的初始特征,使用SDAE进行无监督特征学习,提取初始特征的高层抽象表示。在网络的回归层,使用RELM进行情感分类。在DEAP数据集上的实验结果表明,与SDAE以及DT、KNN等传统基于机器学习的方法相比,该方法在实时性、准确性和泛化性能等方面均有明显提升。     

弹性网络核极限学习机的多标记学习算法

将正则化极限学习机或者核极限学习机理论应用到多标记分类中,一定程度上提高了算法的稳定性。但目前这些算法关于损失函数添加的正则项都基于L2正则,导致模型缺乏稀疏性表达。同时,弹性网络正则化既保证模型鲁棒性且兼具模型稀疏化学习,但结合弹性网络的极限学习机如何解决多标记问题鲜有研究。基于此,本文提出一种对核极限学习机添加弹性网络正则化的多标记学习算法。首先,对多标记数据特征空间使用径向基核函数映射;随后,对核极限学习机损失函数施加弹性网络正则项;最后,采用坐标下降法迭代求解输出权值以得到最终预测标记。通过对比试验和统计分析表明,提出的算法具有更好的性能表现。     

基于天牛群优化与改进正则化极限学习机的网络入侵检测EI北大核心CSCD

正则化极限学习机(Regularized extreme learning machine,RELM)因其极易于实现、训练速度快等优点在诸多领域均取得了成功应用.对此,本文将RELM引入到入侵检测中,设计了天牛群优化算法(Beetle swarm optimization,BSO),并针对RELM由于随机初始化参数带来的潜在缺陷,提出基于天牛群优化与改进正则化极限学习机(BSO-IRELM)的网络入侵检测算法.使用LU分解求解RELM的输出权值矩阵,进一步缩短了RELM的训练时间,同时利用BSO对RELM的权值和阈值进行联合优化.为避免BSO算法陷入局部最优,引入Tent映射反向学习、莱维飞行的群体学习与动态变异策略提升优化性能.实验结果表明,在机器学习UCI数据集上,相比于RELM、IRELM、GA-IRELM、PSO-IRELM等算法,BSO-IRELM的数据分类性能提升明显.最后,将BSO-IRELM应用于网络入侵检测数据集NSL-KDD,并与BP(Back propagation)、LR(Logistics regression)、RBF(Radial basis function)、AB(AdaBoost)、SVM(Support vector machine)、RELM、IRELM等算法进行了对比,结果证明BSO-IRELM算法在准确率、精确率、真正率和假正率等指标上均具有明显优势.     

Multi layer ELM-RBF for multi-label learning

Many neural network methods such as ML-RBF and BP-MLL have been used for multi-label classification. Recently, extreme learning machine (ELM) is used as the basic elements to handle multi-label classification problem because of its fast training time. Extreme learning machine based auto encoder (ELM-AE) is a novel method of neural network which can reproduce the input signal as well as auto encoder, but it can not solve the over-fitting problem in neural networks elegantly. Introducing weight uncertainty into ELM-AE, we can treat the input weights as random variables following Gaussian distribution and propose weight uncertainty ELM-AE (WuELM-AE). In this paper, a neural network named multi layer ELM-RBF for multi-label learning (ML-ELM-RBF) is proposed. It is derived from radial basis function for multi-label learning (ML-RBF) and WuELM-AE. ML-ELM-RBF firstly stacks WuELM-AE to create a deep network, and then it conducts clustering analysis on samples features of each possible class to compose the last hidden layer. ML-ELM-RBF has achieved satisfactory results on single-label and multi-label data sets. Experimental results show that WuELM-AE and ML-ELM-RBF are effective learning algorithms.     

An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances

This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for classifying power system disturbances using particle swarm optimization (PSO). Learning time is an important factor while designing any computational intelligent algorithms for classifications. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are chosen randomly and the output weights are calculated analytically. However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. One of the advantages of ELM over other methods is that the parameter that the user must properly adjust is the number of hidden nodes only. But the optimal selection of its parameter can improve its performance. In this paper, a hybrid optimization mechanism is proposed which combines the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the number of hidden nodes to enhance the performance of ELM. The experimental results showed the proposed algorithm is faster and more accurate in discriminating power system disturbances.     

一种基于正交神经网络的曲线重建方法

提出了一种基于正交神经网络的曲线重建方法。该正交神经网络结构与三层交向网络相同,不同的是正交网的隐单元处理函数采用Ｔｃｈｅｂｙｃｈｅｆｆ正交函数,而不是ｓｉｇｍｏｉｄｉａｌ函数,新的曲线重建方法具有利用较少的数据点列将光滑的曲线以较高的精度重建的特点,网络训练采用Ｇｉｖｅｎｓ正交学习算法,由于它不是一种迭代算法,故学习速度快,而且没有网络初始参数的选取问题,网络训练又能避免陷入局部极小解等问题。实     

Extreme learning machine with errors in variables

Extreme learning machine (ELM) is widely used in training single-hidden layer feedforward neural networks (SLFNs) because of its good generalization and fast speed. However, most improved ELMs usually discuss the approximation problem for sample data with output noises, not for sample data with noises both in input and output values, i.e., error-in-variable (EIV) model. In this paper, a novel algorithm, called (regularized) TLS-ELM, is proposed to approximate the EIV model based on ELM and total least squares (TLS) method. The proposed TLS-ELM uses the idea of ELM to choose the hidden weights, and applies TLS method to determine the output weights. Furthermore, the perturbation quantities of hidden output matrix and observed values are given simultaneously. Comparison experiments of our proposed TLS-ELM with least square method, TLS method and ELM show that our proposed TLS-ELM has better accuracy and less training time.     

一种基于误差的径向基神经网络学习方法

提出了一种基于误差的径向基神经网络竞争学习法，它以网络的输出误差为度量，通过竞争调节神经元中心，RLS算法训练网络的权值，并利用IPL算法判断网络神经元的冗余性。仿真结果表明，该算法提高了网络的输出精度，简化了网络结构，其运算速度也较快。     

Text categorization based on regularization extreme learning machine

This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.