极限学习机的分类器集成模型研究

将极限学习机算法与旋转森林算法相结合,提出了以ELM算法为基分类器并以旋转森林算法为框架的RF-ELM集成学习模型。在8个数据集上进行了3组预测实验,根据实验结果讨论了ELM算法中隐含层神经元个数对预测结果的影响以及单个ELM模型预测结果不稳定的缺陷;将RF-ELM模型与单ELM模型和基于Bagging算法集成的ELM模型相比较,由稳定性和预测精度的两组对比实验的实验结果表明,对ELM的集成学习可以有效地提高ELM模型的性能,且RF-ELM模型较其他两个模型具有更好的稳定性和更高的准确率,验证了RF-ELM是一种有效的ELM集成学习模型。     

Robust visual tracking based on convolutional neural network with extreme learning machine

Multimedia Tools and Applications - Recently, deep learning has attracted substantial attention as a promising solution to many problems in computer vision. Among various deep learning...     

Land-use scene classification based on a CNN using a constrained extreme learning machine

ABSTRACT

Classifying land-use scenes from high-resolution remote-sensing imagery with high quality and accuracy is of paramount interest for science and land management applications. In this article, we proposed a new model for land-use scene classification by integrating the recent success of convolutional neural network (CNN) and constrained extreme learning machine (CELM). In the model, the fully connected layers of a pretrained CNN have been removed. Then, CNN works as a deep and robust convolutional feature extractor. After normalization, deep convolutional features are fed to the CELM classifier. To analyse the performance, the proposed method has been evaluated on two challenging high-resolution data sets: (1) the aerial image data set consisting of 30 different aerial scene categories with sub-metre resolution and (2) a Sydney data set that is a large high spatial resolution satellite image. Experimental results show that the CNN-CELM model improves the generalization ability and reduces the training time compared to state-of-the-art methods.     

结合全卷积神经网络的室内场景分割

目的 视觉假体通过向盲人体内植入电极刺激视神经产生光幻视,盲人所能感受到的物体只是大体轮廓,对物体识别率低,针对视觉假体中室内应用场景的特点,提出一种快速卷积神经网络图像分割方法对室内场景图像进行分割,通过图像分割技术把物品大致的位置和轮廓显示出来,辅助盲人识别。方法 构建了用于室内场景图像分割的FFCN（fast fully convolutional networks）网络,通过层间融合的方法,避免连续卷积对图像特征信息的损失。为了验证网络的有效性,创建了室内环境中的基本生活物品数据集（以下简称XAUT数据集）,在原图上通过灰度标记每个物品的类别,然后附加一张颜色表把灰度图映射成伪彩色图作为语义标签。采用XAUT数据集在Caffe（convolutional architecture for fast feature embedding）框架下对FFCN网络进行训练,得到适应于盲人视觉假体的室内场景分割模型。同时,为了对比模型的有效性,对传统的多尺度融合方法FCN-8s、FCN-16s、FCN-32s等进行结构微调,并采用该数据集进行训练得到适用于室内场景分割的相应算法模型。结果 各类网络的像素识别精度都达到了85%以上,均交并比（MIU）均达到60%以上,其中FCN-8s at-once网络的均交并比最高,达到70.4%,但其分割速度仅为FFCN的1/5。在其他各类指标相差不大的前提下,FFCN快速分割卷积神经网络上平均分割速度达到40帧/s。结论 本文提出的FFCN卷积神经网络可以有效利用多层卷积提取图像信息,避免亮度、颜色、纹理等底层信息的影响,通过尺度融合技术可以很好地避免图像特征信息在网络卷积和池化中的损失,相比于其他FCN网络具有更快的速度,有利于提高图像预处理的实时性。     

Dynamic ensemble extreme learning machine based on sample entropy

Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitting, especially on large datasets. In this paper, a dynamic ensemble extreme learning machine based on sample entropy is proposed, which can alleviate to some extent the problems of instability and over-fitting, and increase the prediction accuracy. The experimental results show that the proposed approach is robust and efficient.     

Hyperspectral remote sensing image classification with information discriminative extreme learning machine

Multimedia Tools and Applications - Hyperspectral remote sensing image classification is important aspect of current research. Extreme learning machine (ELM) has been widely used in the field of...     

基于多输出极限学习机的快速一致性分类器

一致性分类器是建立在一致性预测基础上的分类器,其输出结果具有很高的可靠性,但由于计算框架的限制,学习的时间往往较长.为了加快学习速度,首次将一致性预测与多输出极限学习机相结合,提出基于两者的快速一致性分类算法.该算法利用了极限学习机,能够快速计算样本标签的留一交叉估计的特性,极大地加快了学习速度.算法复杂度分析表明,所提算法的计算复杂度与多输出极限学习机的算法复杂度相同,该算法继承了一致性预测的可靠性特征,即预测的错误率能够被显著性水平参数所控制.在10个公共数据集上的对比实验表明,所提算法具有极快的计算速度,且与其他常用一致性分类器相比,该算法的平均预测标签个数在某些数据集上更少,预测结果更有效.     

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...     

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。     

Background subtraction based on deep convolutional neural networks features

Multimedia Tools and Applications - Background modeling and subtraction, the task to detect moving objects in a scene, is a fundamental and critical step for many high level computer vision tasks....     

Human face recognition based on ensemble of polyharmonic extreme learning machine

This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate.     

Self-supervised learning based on discriminative nonlinear features for image classification

For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach.     

Recognizing learning emotion based on convolutional neural networks and transfer learning

Learning effectiveness is normally analyzed by data collection through tests or questionnaires. However, instant feedback is usually not available. Learners’ facial emotion and learning motivation has a positive relationship. Therefore, the system identifying learners’ facial emotions can provide feedback that teachers can understand students’ learning situation and provide help or improve teaching strategy. Studies have found that convolutional neural networks provide a good performance in basic facial emotion recognition. Convolutional neural networks do not require manual design features like traditional machine learning, they automatically learn the necessary features of the entire image. This article improves the FaceLiveNet network with low and high accuracy in basic emotion recognition, and proposes the framework of Dense_FaceLiveNet. We use Dense_FaceLiveNet for two-phases of transfer learning. First, from the relatively simple data JAFFE and KDEF basic emotion recognition model transferring to the FER2013 basic emotion dataset and obtained an accuracy of 70.02%. Secondly, using the FER2013 basic emotion recognition model transferring to learning emotion recognition model, the test accuracy rate is as high as 91.93%, which is 12.9% higher than the accuracy rate of 79.03% without using the transfer learning model, which proves that the use of transfer learning can effectively improve the recognition accuracy of learning emotion recognition model. In addition, in order to test the generalization ability of the Learning Emotion Recognition Model, videos recorded by students from a national university in Taiwan during class learning were used as test data. The original database of learning emotions did not consider that students would have exceptions such as over eyebrows, eyes closed and hand hold the chin etc. To improve this situation, after adding the learning emotion database to the images of the exceptions mentioned above, the model was rebuilt, and the recognition accuracy rate of the model was 92.42%. By comparing the output of maps, the rebuilt model does have the characteristics of success in learning images such as eyebrows, chins, and eyes closed. Furthermore, after combining all the students’ image data with the original learning emotion database, the model was rebuilt and obtained the accuracy rate reached 84.59%. The result proves that the Learning Emotion Recognition Model can achieve high recognition accuracy by processing the unlearned image through transfer learning. The main contribution is to design two-phase transfer learning for establishing the learning emotion recognition model and overcome the problem for small amounts of learning emotion data. Our experiment results have shown the performance improvement of two-phase transfer learning.     

Improving discrimination ability of convolutional neural networks by hybrid learning

The discrimination of similar patterns is important because they are the major sources of the classification error. This paper proposes a novel method to improve the discrimination ability of convolutional neural networks (CNNs) by hybrid learning. The proposed method embeds a collection of discriminators as well as a recognizer in a shared CNN. By visualizing contrastive class saliency, we show that learning with embedded discriminators leads the shared CNN to detect and catch the differences among similar classes. Also proposed is a hybrid learning algorithm that learns recognition and discrimination together. The proposed method learns recognition focusing on the differences among similar classes, and thereby improves the discrimination ability of the CNN. Unlike conventional discrimination methods, the proposed method does not require predefined sets of similar classes or additional step to integrate its result with that of the recognizer. In experiments on two handwritten Hangul databases SERI95a and PE92, the proposed method reduced classification error from 2.56 to 2.33, and from 4.04 to 3.66 % respectively. These improvement lead to relative error reduction rates of 8.97 % on SERI95a, and 9.42 % on PE92. Our best results update the state-of-the-art performance which were 4.04 % on SERI95a and 7.08 % on PE92.     

优化的支持向量机集成分类器在非平衡数据集分类中的应用

传统的分类算法大都建立在平衡数据集的基础上,当样本数据不平衡时,这些学习算法的性能往往会明显下降.对于非平衡数据分类问题,提出了一种优化的支持向量机(SVM)集成分类器模型,采用KSMOTE和Bootstrap对非平衡数据进行预处理,生成相应的SVM模型并用复合形算法优化模型参数,最后利用优化的参数并行生成SVM集成分类器模型,采用投票机制得到分类结果.对5组UCI标准数据集进行实验,结果表明采用优化的SVM集成分类器模型较SVM模型、优化的SVM模型等分类精度有了明显的提升,同时验证了不同的bootNum取值对分类器性能效果的影响.     

基于改进的卷积神经网络的中文情感分类

探究了基于卷积神经网络的句子级别的中文文本情感分类,模型以文本经过预处理后得到的词向量作为输入。传统的卷积神经网络是由线性卷积层、池化层和全连接层堆叠起来的,提出以跨通道卷积层替代传统线性卷积滤波器,对基本的卷积神经网络进行改进,提高网络的表达能力。实验表明,改进后的卷积神经网络在保证训练速度的情况下,识别率达到91.89%,优于传统的卷积神经网络,有较好的识别能力。     

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images

Neural Computing and Applications - Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of...     

Image classification based on convolutional neural networks with cross-level strategy

Multimedia Tools and Applications - In the past few years, convolutional neural networks (CNNs) have exhibited great potential in the field of image classification. In this paper, we present a...     

Attacking neural machine translations via hybrid attention learning

Machine Learning - Deep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks...     

Convolutional neural networks and extreme learning machines for malware classification

Journal of Computer Virology and Hacking Techniques - Research in the field of malware classification often relies on machine learning models that are trained on high-level features, such as...