基于随机森林优化的自组织神经网络算法

针对基于降维的神经网络分类器预测模型在分析过程中存在特征丢失, 并导致精度下降的问题， 提出一种基于随机森林算法优化的多层感知器(MLP)回归预测模型. 该优化模型通过在MLP回归模型网络的全连接层和逻辑回归层之间增加一个优化机制, 利用随机森林算法对隐藏层状态的优化实现改进, 从而解决了降维过程中神经网络丢失数据特征的问题. 在借贷客户信息数据集上的实验结果表明, 该模型在保证主要特征的同时大幅度提升了预测准确率, 证实该模型在特征工程中具有较高的实用性.     

SmartCare: Energy-Efficient Long-Term Physical Activity Tracking Using Smartphones

Lack of physical activity is becoming a killer of our healthy life. As a solution for this negative impact,we propose Smart Care to help users to set up a healthy physical activity habit. Smart Care can monitor a user's activities over a long time, and then provide activity quality assessment and suggestion. Smart Care consists of three parts, activity recognition, energy saving, and health feedback. Activity recognition can recognize nine kinds of daily activities. A hybrid classifier that uses less power and memory with satisfactory accuracy was designed and implemented by utilizing the periodicity of target activity. In addition, a learning-based energy saver was introduced to reduce energy consumption by adjusting sampling rates and the set of features adaptively. Based on the type and duration of the activity recorded, health feedback in terms of the calorie burned was given. The system could provide quantitative activity quality assessment and recommend future physical activity plans. Through extensive real-life testing, the system is shown to achieve an average recognition accuracy of 98.0% with a minimized energy expenditure.     

一种基于BP-bagging的手写体数字识别算法

基于BP-bagging的手写体数字识别算法,以BP网络做基本分类器,用Bagging产生多个基本分类器,把每一个手写体数字作为一幅图像,通过扫描提取25维特征,并投影压缩生成5维特征向量,并输入到BP-bagging分类器,用简单多数投票法进行集成,实现手写体数字的分类识别.经反复的实验证明,该手写体数字识别算法具有...     

降维近似支持向量机基因芯片数据分类器

基因芯片技术的出现改变了生物医学研究的前景,其产生的海量数据是限制其发展的瓶颈问题。论文针对基因芯片数据量大、样本数低和基因维数高的特点,提出了一种对基因芯片数据进行分类的降维近似支持向量机DRPSVM基因芯片数据分类器。DRPSVM采用降维的二次规划算法,使得该算法的时间复杂度和空间复杂度比传统的PSVM算法均有降低。通过在CAMDA2000、colon 1 dataset和colon 2 dataset等基因芯片数据集上的与BP、Nearest、RBF、SVM分类器的分类性能比较,DRPSVM在数据样本少、数据维数急剧升高时,分类性能稳定、存在唯一的最优解、训练时间快,适合基因芯片数据分类的应用环境。     

基于网络评论语言学结构的情感倾向识别模型

展示了一种新的基于网络评论语言学结构的情感倾向识别模型,固定情感词元模型（fixed sentiment terms model）.该方法利用基于固定情感词元的3种特定搭配模式来构造识别算法,通过基于增量的tf-idf模型的相关用户反馈不断更新特征词元集合.通过与传统的情感识别方法相比较,此方法可以较为明显地提高情感分类的效率和准确率.     

基于图的Laplace矩阵和非负矩阵的图像分类

文章将图的Laplace矩阵和非负矩阵分解方法结合起来,应用于图像分类.对不同的图像先提取其特征点,再对提取得到的特征点构造图的Laplace矩阵,将构造的矩阵进行非负矩阵分解后得到图像的特征向量,最后将特征向量输入到PNN分类器中,对图像进行分类.对模拟图像和真实图像进行了多组实验,结果证明了该算法应用于图像分类的准...     

基于OpenCV和Haar特征分类器的图像人数检测

研究一种基于OpenCV和Haar特征检测固定区域图像中人数的方法。通过选取大量含有人的Haar特征的样本图片,利用OpenCV训练出分类器,并通过实验深入分析选取的样本对分类器识别性能(即检测效果)的影响,最终选取人的头肩部上半身样本训练分类器,并在1 500张640pix×480pix待检图像(共计人数17 294人)的检测实验中达到93.9%的识别准确率,平均检测时间小于323ms。     

Research on remote sensing image of land cover classification based on multiple classifier combination

This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing, we test the algorithm on a China-Brazil Earth Resources Satellite (CBERS) image for land cover classification. The results show that using three computers in parallel can reduce the classification time by 30%, as compared with using only one computer with a dual core processor. The accuracy of the final image is 93.34%, and Kappa is 0.92. Multiple classifier combination can enhance the precision of the image classification, and parallel computing can increase the speed of calculation so that it becomes possible to process remote sensing images with high efficiency and accuracy.     

起重机械检验数据挖掘系统的设计与实现

利用数据挖掘技术,开发了起重机械检验数据挖掘系统,为检验起重机械安全运行性提供了一种有效决策模型。系统采用决策树分类,实现了对起重机械的故障诊断;利用关联规则,对起重机械缺陷、失效进行相关性分析。系统运行结果表明,该系统具有一定的应用价值。