A committee machine with a set of networks composed of two single-threshold elements as committee members

We propose a committee machine whose each committee member is a network of two threshold elements. This improves upon the ability of the usual committee machine. The subsidiary discriminant function in this case is a kind of piecewise linear discriminant function instead of a linear function. We show a sophisticated representation of the discriminant function realized by the proposed committee machine, and give a rational learning algorithm based on the function. We also show that the proposed committee machine, which is a kind of three-layer network of threshold elements, cannot be equivalently transformed to a two-layer network. This fact asserts that the proposed committee machine provides a family of discriminant functions which is intrinsically wider than that of the usual committee machine.     

A general method for training the committee machine

A method for training the committee machine with an arbitrary logic is described. First, an expression of the discriminant function realized by the committee machine is introduced. By making use of the expression, an error-correction procedure for training the committee machine is proposed. The procedure of the perceptron is clearly explained as the special case of the proposed procedure. Experimental results show that the procedure is effective.     

应用于人脸图像识别的邻域保持极限学习机

极限学习机广泛应用于人脸识别领域。传统的极限学习机算法因在少量标签样本上进行训练，容易发生学习过程不充分问题，同时在学习过程中往往忽略了样本内在的几何结构，影响其对人脸识别的分类能力。受流形学习思想的启发，提出一种邻域保持极限学习机算法。该算法保持数据最本质的结构和同类数据的判别信息，利用最小化类内散度矩阵来提高极限学习机整体的分类性能。通过人脸数据集上的多次实验结果表明，该算法的人脸识别准确率高于其他算法，更能有效地进行分类识别。     

Missing multi-label learning with non-equilibrium based on two-level autoencoder

For a multi-label learning framework, each instance may belong to multiple labels simultaneously. The classification accuracy can be improved significantly by exploiting various correlations, such as label correlations, feature correlations, or the correlations between features and labels. There are few studies on how to combine the feature and label correlations, and they deal more with complete data sets. However, missing labels or other phenomena often occur because of the cost or technical limitations in the data acquisition process. A few label completion algorithms currently suitable for missing multi-label learning, ignore the noise interference of the feature space. At the same time, the threshold of the discriminant function often affects the classification results, especially those of the labels near the threshold. All these factors pose considerable difficulties in dealing with missing labels using label correlations. Therefore, we propose a missing multi-label learning algorithm with non-equilibrium based on a two-level autoencoder. First, label density is introduced to enlarge the classification margin of the label space. Then, a new supplementary label matrix is augmented from the missing label matrix with the non-equilibrium label completion method. Finally, considering feature space noise, a two-level kernel extreme learning machine autoencoder is constructed to implement the information feature and label correlation. The effectiveness of the proposed algorithm is verified by many experiments on both missing and complete label data sets. A statistical analysis of hypothesis validates our approach.

     

面向类别不平衡数据的主动在线加权极限学习机算法

针对在样本类别分布不平衡场景下,现有的主动学习算法普遍失效及训练时间过长等问题,提出采用建模速度更快的极限学习机,即ELM(Extreme Learning Machine)作为主动学习的基分类器,并以加权ELM算法用于主动学习过程的平衡控制,进而在理论上推导了其在线学习的过程,大幅降低了主动学习的时间开销,并将最终的混合算法命名为AOW-ELM算法。通过12个基准的二类不平衡数据集验证了该算法的有效性与可行性。     

融合异构特征的子空间迁移学习算法

特征迁移重在领域共有特征间学习,然而其忽略领域特有特征的判别信息,使算法的适应性受到一定的局限. 针对此问题,提出了一种融合异构特征的子空间迁移学习（The subspace transfer learning algorithm integrating with heterogeneous features,STL-IHF）算法.该算法将数据的特征空间看成共享和特有两个特征子空间的组合,同时基于经验风险最 小框架将共享特征和特有特征共同嵌入到支持向量机（Support vector machine,SVM）的训练过程中.其在共享特征子空间上实现知识迁移的 同时兼顾了领域特有的异构信息,增强了算法的适应性.模拟和真实数据集上的实验结果表明了所提方法的有效性.     

Physics-based machine learning method and the application to energy consumption prediction in tunneling construction

Representing causality in machine learning to predict control parameters is state-of-the-art research in intelligent control. This study presents a physics-based machine learning method providing a prediction model that guarantees enhanced interpretability conforming to physical laws. The proposed approach encodes physical knowledge as mapping relationships between variables in engineering dataset into the learning procedure through dimensional analysis. This derives causal relationships between the control parameter and its influencing factors. The proposed machine learning method's objective function is further improved by the penalty term in the regularization strategy. Verifications on the energy consumption prediction of tunnel boring machine prove that, the established model accords with basic principles in this field. Moreover, the proposed approach traces the impact of three major factors (structure, operation, and geology) along the construction section, offering each component's contribution rates to energy consumption. Compared with several commonly used machine learning algorithms, the proposed method reduces the need for large amounts of training data and demonstrates higher accuracy. The results indicate that the revealed causality and enhanced prediction performance of the proposed method advance the applicability of machine learning methods to intelligent control during construction.     

进化贝叶斯优化的核极限学习机分类器

为解决传统核极限学习机算法参数优化困难的问题,提高分类准确度,提出一种改进贝叶斯优化的核极限学习机算法.用樽海鞘群设计贝叶斯优化框架中获取函数的下置信界策略,提高算法的局部搜索能力和寻优能力;用这种改进的贝叶斯优化算法对核极限学习机的参数进行寻优,用最优参数构造核极限学习机分类器.在UCI真实数据集上进行仿真实验,实验...     

A study on effectiveness of extreme learning machine

Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs). Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM. This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory. This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks. The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM.     

一种新的有监督流形学习方法

提出了一种新的有监督流形学习方法,目的是提供将流形学习降维方法高效应用于有监督学习问题的全新策略.算法的核心思想是集成流形学习方法对高维流形结构数据的降维有效性与支撑向量机(SVM)在中小规模分类数据集上的优良特性实现高效有监督流形学习.算法具体实现步骤为:首先利用SVM在流形学习降维数据中选出对分类决策最重要的数据集,即支撑向量集;按标号返回可得到原空间的支撑向量集;在这个集合上再次使用SVM即可得到原空间的分类决策,从而完成有监督流形学习.在一系列人工与实际数据集上的实验验证了方法的有效性.     

Least Square Transduction Support Vector Machine

Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning principle such as transductive learning has attracted more and more attentions. Transductive support vector machine (TSVM) learns a large margin hyperplane classifier using labeled training data, but simultaneously force this hyperplane to be far away from the unlabeled data. TSVM might seem to be the perfect semi-supervised algorithm since it combines the powerful regularization of SVMs and a direct implementation of the clustering assumption, nevertheless its objective function is non-convex and then it is difficult to be optimized. This paper aims to solve this difficult problem. We apply least square support vector machine to implement TSVM, which can ensure that the objective function is convex and the optimization solution can then be easily found by solving a set of linear equations. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.     

A complete fuzzy discriminant analysis approach for face recognition

In this paper, some studies have been made on the essence of fuzzy linear discriminant analysis (F-LDA) algorithm and fuzzy support vector machine (FSVM) classifier, respectively. As a kernel-based learning machine, FSVM is represented with the fuzzy membership function while realizing the same classification results with that of the conventional pair-wise classification. It outperforms other learning machines especially when unclassifiable regions still remain in those conventional classifiers. However, a serious drawback of FSVM is that the computation requirement increases rapidly with the increase of the number of classes and training sample size. To address this problem, an improved FSVM method that combines the advantages of FSVM and decision tree, called DT-FSVM, is proposed firstly. Furthermore, in the process of feature extraction, a reformative F-LDA algorithm based on the fuzzy k-nearest neighbors (FKNN) is implemented to achieve the distribution information of each original sample represented with fuzzy membership grade, which is incorporated into the redefinition of the scatter matrices. In particular, considering the fact that the outlier samples in the patterns may have some adverse influence on the classification result, we developed a novel F-LDA algorithm using a relaxed normalized condition in the definition of fuzzy membership function. Thus, the classification limitation from the outlier samples is effectively alleviated. Finally, by making full use of the fuzzy set theory, a complete F-LDA (CF-LDA) framework is developed by combining the reformative F-LDA (RF-LDA) feature extraction method and DT-FSVM classifier. This hybrid fuzzy algorithm is applied to the face recognition problem, extensive experimental studies conducted on the ORL and NUST603 face images databases demonstrate the effectiveness of the proposed algorithm.     

基于随机近邻嵌入的判别性特征学习

特征学习是机器学习中的一项重要技术,研究从原始数据中学习后置任务所需的数据表示.目前,多数特征学习算法侧重于学习原始数据中的拓扑结构,忽略了数据中的判别信息.基于此,提出了基于随机近邻嵌入的判别性特征学习模型.该模型将对判别信息的学习与对拓扑结构的学习融合在一起,通过迭代求解的方式,同时完成对这两者的学习,从而得到原始...     

基于核字典学习的图像分类

航拍图像往往具有场景复杂、数据维度大的特点,对于该类图像的自动分类一直是研究的热点。针对航拍原始数据特征维度过高和数据线性不可分的问题,在字典学习和稀疏表示的基础上提出了一种结合核字典学习和线性鉴别分析的目标识别方法。首先学习核字典并通过核字典获取目标样本的稀疏表示,挖掘数据的内部结构;其次采用线性鉴别分析,加强稀疏表示的可分性;最后利用支持向量机对目标进行分类。实验结果表明,与传统基于子空间特征提取的算法和基于字典学习的算法相比,基于核字典学习与鉴别分析的算法分类性能优越。     

基于- 范数约束的LSSVR 多核学习算法

针对核函数选择对最小二乘支持向量机回归模型泛化性的影响, 提出一种新的基于????- 范数约束的最小二乘支持向量机多核学习算法. 该算法提供了两种求解方法, 均通过两重循环进行求解, 外循环用于更新核函数的权值, 内循环用于求解最小二乘支持向量机的拉格朗日乘数, 充分利用该多核学习算法, 有效提高了最小二乘支持向量机的泛化能力, 而且对惩罚参数的选择具有较强的鲁棒性. 基于单变量和多变量函数的仿真实验表明了所提出算法的有效性.

     

Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.     

Lp范数约束的多核半监督支持向量机学习方法

在机器学习领域,核方法是解决非线性模式识别问题的一种有效手段.目前,用多核学习方法代替传统的单核学习已经成为一个新的研究热点,它在处理异构、不规则和分布不平坦的样本数据情况下,表现出了更好的灵活性、可解释性以及更优异的泛化性能.结合有监督学习中的多核学习方法,提出了基于L_p范数约束的多核半监督支持向量机(semi-supervised support vector machine,简称S³VM)的优化模型.该模型的待优化参数包括高维空间的决策函数f_m和核组合权系数θm.同时,该模型继承了单核半监督支持向量机的非凸非平滑特性.采用双层优化过程来优化这两组参数,并采用改进的拟牛顿法和基于成对标签交换的局部搜索算法分别解决模型关于f_m的非平滑及非凸问题,以得到模型近似最优解.在多核框架中同时加入基本核和流形核,以充分利用数据的几何性质.实验结果验证了算法的有效性及较好的泛化性能.     

一种改进的SVM回归估计算法

支持向量机是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法。目前,如何设计快速有效的回归估计算法仍然是支持向量机实际应用中的问题之一。文中对标准SVM回归估计算法加以改进,提出一种改进的SVM回归估计算法,并从学习速度和回归估计精度两个方面对提出的改进的SVM回归估计算法与标准SVM回归估计算法进行了比较。实验结果表明,在学习速度与回归估计精度之间取折衷时,文中提出的回归估计算法自由度更大。     

Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of ?1?a?a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.