基于非对称打包和FSVM的图像检索

在图像检索的相关反馈中,引入支持向量机分类方法虽可以提升图像的检索性能,但是传统的支持向量机存在正样本数少、样本非对称、过学习和弱实时性的局限。针对上述问题,提出了一种基于非对称打包的FSVM算法。该算法首先对负样本进行非对称打包处理,最后结合模糊理论与SVM实现图像检索。Corel图片集上的实验表明,当正样本数较小时,该新算法的平均查准率-查全率要优于已有算法。     

Bagging中文文本分类器的改进方法研究

在文本分类研究中,集成学习是一种提高分类器性能的有效方法.Bagging算法是目前流行的一种集成学习算法.针对Bagging算法弱分类器具有相同权重问题,提出一种改进的Bagging算法.该方法通过对弱分类器分类结果进行可信度计算得到投票权重,应用于Attribute Bagging算法设计了一个中文文本自动分类器.采用kNN作为弱分类器基本模型对Sogou实验室提供的新闻集进行分类.实验表明该算法比Attribute Bagging有更好的分类精度.     

基于C4．5算法的集成方法研究

对于一些数据量有限的数据集而言,如何提高分类精度是机器学习研究者们的主要任务之一.集成学习方法通过同时构造多个学习器,然后对各学习器的分类结果使用投票法得到分类结果.提出了基于Bagging的C4.5集成算法,实验结果表明:采用这种方法会有效提升分类能力.     

全自动硫磺灌包机防堵转系统设计

本文针对全自动硫磺灌包机在灌包过程中,破碎机系统易发生堵转,造成全自动硫磺灌包机停机甚至破碎电机过热烧毁的情况,提出了一个防堵转的控制系统设计。该设计通过行程开关判断破碎系统是否堵转,简单、可靠,对生产设备设计和改造具有一定的意义。     

Random Sampling for Subspace Face Recognition

Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.     

Bagging Equalizes Influence

Bagging constructs an estimator by averaging predictors trained on bootstrap samples. Bagged estimates almost consistently improve on the original predictor. It is thus important to understand the reasons for this success, and also for the occasional failures. It is widely believed that bagging is effective thanks to the variance reduction stemming from averaging predictors. However, seven years from its introduction, bagging is still not fully understood. This paper provides experimental evidence supporting the hypothesis that bagging stabilizes prediction by equalizing the influence of training examples. This effect is detailed in two different frameworks: estimation on the real line and regression. Baggings improvements/deteriorations are explained by the goodness/badness of highly influential examples, in situations where the usual variance reduction argument is at best questionable. Finally, reasons for the equalization effect are advanced. They support that other resampling strategies such as half-sampling should provide qualitatively identical effects while being computationally less demanding than bootstrap sampling.     

基于集成修剪的丁苯橡胶聚合转化率软测量

针对目前丁苯橡胶聚合转化率难以在线精确测量,不利于指导生产的问题,本文提出一种基于集成修剪的软测量方法用于丁苯橡胶聚合转化率的预测.首先采用bagging方法建立多个LS-SVM弱学习器,然后利用AdaBoost.RT方法对弱学习器进行修剪,最后将修剪出的弱学习器加权输出.该方法克服了集成算法需要存储空间大和预测时间长的缺点,并且在一定程度上改善了最小二乘支持向量机的稀疏性和鲁棒性问题.仿真结果表明,聚合转化率预报绝对误差大于1.5样本的比例小于10%,能够满足实际生产要求,可以作为过程信息用于丁苯橡胶聚合过程的优化控制.     

基于PLC控制的包装码垛生产线的设计

我国的聚丙烯(PP)产量占世界产量的10%以上,而长期以来国内聚丙烯产品的包装却一直处于人工套袋的半自动包装状态。针对PP树脂的特点,专门设计了全自动包装码跺生产线工艺流程,介绍了以PLC为基础的全自动包装码跺生产线控制系统,重点说明了控制系统的硬件配置、输入输出分配和软件设计。全自动包装码垛线改变了国内PP产品包装的落后局面,值得在PP包装领域推广。     

电力系统暂态稳定评估组合模型的比较

组合模型能提高电力系统暂态稳定评估的分类性能。文中构建了12组输入特征,在IEEE 16节点和IEEE 50节点测试系统上生成了24个样本集。提出了一种测试分类综合指标。在24个样本集上比较了单个(神经网络、决策树、K最近邻法和支持向量机)和组合(装袋、提升、堆栈和随机森林)的暂态稳定评估模型测试指标发现,单个评估模型中,K最近邻法分类性能最好;组合方法均能提高分类性能,其中随机森林分类性能最好,其次是堆栈(支持向量机、K最近邻法、决策树)、提升-决策树和装袋-决策树。