一适用于超多类手写汉字识别的新改型Adaboost算法

提出一种适用于超多类手写汉字识别的新改型Adaboost算法,采用基于描述性模型的多类分类器（modified quadratic discriminant function,MQDF）作为Adaboost基元分类器,可直接进行多类分类,无需将多类问题转化为多个两类问题处理,其训练复杂度大大低于已有的多类Adaboost算法。算法提出根据广义置信度更新样本权重,实验证明这种算法适用于大规模多类分类问题。为了降低算法的识别复杂度,提出从所有训练后得到的Adaboost基元分类器组中选择一个最优的基元分类器作为最终分类器的方法进行删减。在HCL2000及THOCR-HCD数据集上进行实验证明,所提改型Adaboost算法提高了识别率的有效性,该算法的相对错误率比现有最优算法分别下降了14.3 %,8.1 %和19.5 %。     

一种适用于超多类手写汉字识别的新改型Adaboost算法

提出一种适用于超多类手写汉字识别的新改型Adaboost算法,采用基于描述性模型的多类分类器（modified quadratic discriminant function,MQDF）作为Adaboost基元分类器,可直接进行多类分类,无需将多类问题转化为多个两类问题处理,其训练复杂度大大低于已有的多类Adaboost算法。算法提出根据广义置信度更新样本权重,实验证明这种算法适用于大规模多类分类问题。为了降低算法的识别复杂度,提出从所有训练后得到的Adaboost基元分类器组中选择一个最优的基元分类器作为最终分类器的方法进行删减。在HCL2000及THOCR—HCD数据集上进行实验证明,所提改型Adaboost算法提高了识别率的有效性,该算法的相对错误率比现有最优算法分别下降了14．3％．8．1％和19．5％．     

基于Adaboost算法选取和组合SVM的行人检测算法

行人检测是计算机视觉中一个重要的研究方向,为了提高行人的识别精度,将支持向量机(Sup-port Vector Machine,SVM)和Adaboost算法结合起来,SVM是基于结构风险最小化准则的新型机器学习算法,适合小样本学习并且能够有效地抑制过拟合问题,Adaboost基于最小化训练错误率,一般使用易训练的分类器作为弱分类器.由于SVM比较难训练,因此将样本集划分形成多个训练集,然后利用正样本和不同的负样本组成不同训练集反复训练,最后通过Adaboost对训练集生成的SVM模型筛选出具有最小错误率的SVM分类器并且采用投票机制形成最终的强分类器.实验结果表明,在FPPW(false positive per window)为10-5时检测率能够达到30%,检测效果优于单个SVM算法训练出来的分类器模型,用行人测试库测试,该方法取得了较好的检测效果并且具有较强的鲁棒性.     

基于共形几何代数与二次规划的分类器设计

提出一种基于共形几何代数与二次规划的分类器设计方法.从新的角度出发,讨论了运用共形几何代数理论来构造最优分类超球可分问题的可行性和简便性,首先介绍了基于共形几何代数的分类超球面的几何表示,并用此表示将二类最优分类超球面的可分问题转化二次规划的训练学习问题,在此基础上分析了多类分类器的设计和训练方法.该算法保留了最大分类间隔理论的优点,将二类最优平面可分推广到最优超球可分,简化了其运算复杂度,仿真实验表明,该学习算法简洁明确,对于算法的集成,提高效率有着很重要的意义.     

异构分类器融合环境下的非平衡数据分类模型

为解决非平衡数据分类中的正样本分类精度不高的瓶颈问题,提出了一种异构分类器融合环境下的非平衡数据分类模型.该模型基于差异采样率的重采样算法和改进的Adaboost算法,融合了SVM和C5.0两种基分类器;基于知识融合机制,采用了独特的分类器选择策略、分类器集成方法、分类决策方案.仿真实验结果表明,SCECM模型分类性能...     

基于多核Boosting方法的通用目标分类和识别

针对传统基于SVM分类器的多核学习方法优化参数多、优化过程复杂、计算量大的缺点,本文提出基于Real Adaboost的多核学习方法解决通用目标分类与识别问题.该方法根据核函数能将高维特征映射到低维空间的特性,采用核函数空间上的线性平面分割构建弱分类器,并用Real Adaboost学习框架对弱分类器进行学习.先用分层...     

基于AlexNet-Adaboost的多工况滚动轴承故障识别方法

针对实际工程中滚动轴承多工况下传统故障诊断方法识别率偏低的情况.提出了一种基于AlexNet-Adaboost相结合的滚动轴承故障识别方法.以滚动轴承信号的时频图作为模型输入、分类结果作为模型输出,训练多个AlexNet基分类器;在此基础上利用Adaboost(自适应提升)算法进一步提升得到强分类器,将多工况下滚动轴承...     

自适应免疫算法的SVME用于水下目标识别

针对用支持向量机集成提高水下目标识别正确率会使识别系统更加复杂的问题,提出了一种以自适应免疫算法(AIA)的支持向量机选择性集成(SVME)算法(即AIA-SVME算法)进行分类器优化选择,对实测水下目标声信号进行分类识别.与分类器全部集成的识别实验对比证明,该算法在选择9%的分类器后仍可以达到分类器全部集成的识别效果,不仅保证了识别精度,还使得识别系统大幅度精简,节省在线识别的时间.该研究对于水下目标分类决策优化集成的新方法探索具有重要理论价值和实际意义.     

集成轮廓跟踪

本文将跟踪看作是二分类问题,提出了一种基于Adaboost集成学习和快速水平集的轮廓跟踪算法.该方法首先在线地训练一个弱分类器的集合用以区分目标和背景,而通过Adaboost将集合中的各弱分类器组合成一个强分类器,并用于标定下一帧中的各像素的类别属性,从而确定快速水平集算法的速度函数,然后采用基于动态邻近区域快速水平集来演化目标边界曲线以实现目标的轮廓跟踪.为适应目标和背景的变化,在跟踪过程中在线训练新的弱分类器,而时间相关性则通过更新包含新弱分类器的集合来实现.实验结果表明,在摄像机运动、光照变化,部分遮挡或目标尺度变化等情况下,能实现刚体或非刚体目标的轮廓跟踪.     

基于集成分类器的泡罩包装药品缺陷识别

目的 针对药品生产包装过程中常出现缺陷泡罩包装药品的问题,研究一种基于多特征构建与集成分类器的泡罩包装药品缺陷识别方法.方法 该方法通过集成2个不同的分类器算法分别对药品图像类别进行预测,并采用联合判定函数对2个预测输出值进行联合决策,得到最终分类结果.第1个分类器模型通过将图像转化到HSV颜色空间,分割出泡罩区域和药片区域,进行特征设计,并在提取多项特征参数后构建BP神经网络分类算法给定药品类别预测.第2个分类器模型应用多层卷积神经网络取代传统算法对图像特征进行提取,并输出药品图像类别的预测值.根据2个分类器的性能进行算法集成,构成最终集成分类器.结果 实验结果表明,该集成分类模型对数据集中泡罩包装药品图像进行分类识别测试,准确率达97％以上.结论 集成分类模型不仅提高了单一分类器的识别准确率,也具有更佳的稳定性.该方法取得了卓越的分类效果,具有较高应用性.     

Performance analysis of EM,SVD, and SVM classifiers in classification of carcinogenic regions of medical images

In this article, the performance analysis of Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM) classifiers for classification of carcinogenic regions from various medical images is carried out. Cancer detection is one of the critical issues where excessive care needs to be taken for better diagnosis. Any classifier needs to detect the cancer with respect to the efficiency in time of detection and performance. Due to these, three classifiers are selected: Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM). EM classifier performs as the optimizer and SVD classifier performs as the dual class classifier. SVM classifier is used as both optimizer and classifier for multiclass classification procedure and for wide stage cancer detection procedures. The performance analysis of all the three classifiers are analyzed for a group of 100 cancer patients based on the benchmark parameter such as Performance Measures and Quality Metrics. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.     

印刷套准识别方法研究

目的研究印刷标志套准机器快速和高精度的识别方法。方法提取印刷标志图像的灰度共生矩阵表达其纹理特征,采用Adaboost分类器对印刷标志套准图像进行识别,以判断印刷是否套准。结果提取出了印刷标志图像的能量、熵、惯性矩、相关度等的均值和标准差的8维图像纹理特征。为了比较不同分类器的分类性能,分别得出了Adaboost、K近邻、贝叶斯、支持向量机、Fisher和决策树对印刷标志图像纹理特征的分类准确率和分类时间。结论采用文中方法,印刷标志图像套准识别准确率达到97.5%,分类时间达到0.022 377 s,优于其他的分类方法。     

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme learning machine and Adaboost algorithm, which has three main advantages: (1) KPCA can extract optimal representative features by leveraging a nonlinear mapping function; (2) We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM, so as to improve the generalization ability and prediction capacity of ELM; (3) We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor, which can further improve the effect of defect prediction. To effectively evaluate the performance of KAEA, we use eleven datasets from large open source projects, and compare the KAEA with four machine learning basic classifiers, ELM and its three variants. The experimental results show that KAEA is superior to these baseline models in most cases.     

Emotion recognition based on facial components

Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human–computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from these expressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques.     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

A Multicategory Kernel Distance Weighted Discrimination Method for Multiclass Classification

Abstract

Distance weighted discrimination (DWD) is an interesting large margin classifier that has been shown to enjoy nice properties and empirical successes. The original DWD only handles binary classification with a linear classification boundary. Multiclass classification problems naturally appear in various fields, such as speech recognition, satellite imagery classification, and self-driving vehicles, to name a few. For such complex classification problems, it is desirable to have a flexible multicategory kernel extension of the binary DWD when the optimal decision boundary is highly nonlinear. To this end, we propose a new multicategory kernel DWD, that is, defined as a margin-vector optimization problem in a reproducing kernel Hilbert space. This formulation is shown to enjoy Fisher consistency. We develop an accelerated projected gradient descent algorithm to fit the multicategory kernel DWD. Simulations and benchmark data applications are used to demonstrate the highly competitive performance of our method, as compared with some popular state-of-the-art multiclass classifiers.