To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kerne...To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.展开更多
将OpenMP并行运算库和Intel Math Kernel Library10.2科学计算库运用到高阶地球重力场反演,显著提高了计算效率。模拟结果表明:1)在以单历元为解算单元形成子法方程系数矩阵时,OpenMP可下三角压缩存储,其内存销耗和时间销耗均比较小;2)...将OpenMP并行运算库和Intel Math Kernel Library10.2科学计算库运用到高阶地球重力场反演,显著提高了计算效率。模拟结果表明:1)在以单历元为解算单元形成子法方程系数矩阵时,OpenMP可下三角压缩存储,其内存销耗和时间销耗均比较小;2)当利用多个历元组成高维系数矩阵,然后再形成法方程时,MKL算法才能体现出高效性能;3)MKL求逆算法效率远高于OpenMP算法;4)综合利用OpenMP和MKL算法的优势,可显著提高高阶地球重力场反演的效率。展开更多
识别虚假评论有着重要的理论意义与现实价值.先前工作集中于启发式策略和传统的全监督学习算法.最近研究表明:人类无法通过先验知识有效识别虚假评论,手工标注的数据集必定存在一定数量的误例,因此简单使用传统的全监督学习算法识别虚...识别虚假评论有着重要的理论意义与现实价值.先前工作集中于启发式策略和传统的全监督学习算法.最近研究表明:人类无法通过先验知识有效识别虚假评论,手工标注的数据集必定存在一定数量的误例,因此简单使用传统的全监督学习算法识别虚假评论并不合理.容易被错误标注的样例称为间谍样例,如何确定这些样例的类别标签将直接影响分类器的性能.基于少量的真实评论和大量的未标注评论,提出一种创新的PU(positive and unlabeled)学习框架来识别虚假评论.首先,从无标注数据集中识别出少量可信度较高的负例.其次,通过整合LDA(latent Dirichlet allocation)和K-means,分别计算出多个代表性的正例和负例.接着,基于狄利克雷过程混合模型(Dirichlet process mixture model,DPMM),对所有间谍样例进行聚类,混合种群性和个体性策略来确定间谍样例的类别标签.最后,多核学习算法被用来训练最终的分类器.数值实验证实了所提算法的有效性,超过当前的基准.展开更多
文摘To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.
文摘将OpenMP并行运算库和Intel Math Kernel Library10.2科学计算库运用到高阶地球重力场反演,显著提高了计算效率。模拟结果表明:1)在以单历元为解算单元形成子法方程系数矩阵时,OpenMP可下三角压缩存储,其内存销耗和时间销耗均比较小;2)当利用多个历元组成高维系数矩阵,然后再形成法方程时,MKL算法才能体现出高效性能;3)MKL求逆算法效率远高于OpenMP算法;4)综合利用OpenMP和MKL算法的优势,可显著提高高阶地球重力场反演的效率。
文摘识别虚假评论有着重要的理论意义与现实价值.先前工作集中于启发式策略和传统的全监督学习算法.最近研究表明:人类无法通过先验知识有效识别虚假评论,手工标注的数据集必定存在一定数量的误例,因此简单使用传统的全监督学习算法识别虚假评论并不合理.容易被错误标注的样例称为间谍样例,如何确定这些样例的类别标签将直接影响分类器的性能.基于少量的真实评论和大量的未标注评论,提出一种创新的PU(positive and unlabeled)学习框架来识别虚假评论.首先,从无标注数据集中识别出少量可信度较高的负例.其次,通过整合LDA(latent Dirichlet allocation)和K-means,分别计算出多个代表性的正例和负例.接着,基于狄利克雷过程混合模型(Dirichlet process mixture model,DPMM),对所有间谍样例进行聚类,混合种群性和个体性策略来确定间谍样例的类别标签.最后,多核学习算法被用来训练最终的分类器.数值实验证实了所提算法的有效性,超过当前的基准.
