In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluste...In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.展开更多
Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technol...Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in proteomics.In this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature subset.Finally,GTB-PPI model is constructed.Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,respectively.In addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling pathways.The results show that GTB-PPI can significantly improve accuracy of PPI prediction.The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.展开更多
为探究含硫量对硫化矿粉尘云最小点火能的影响,将A、B、C 3组不同含硫量的硫化矿粉尘云于20 L爆炸球中测定最小点火能。所得实验数据用点火概率(是否着火)表示最小点火能的方法,得出各组硫化矿粉尘云着火概率随点火能变化的分布曲线。...为探究含硫量对硫化矿粉尘云最小点火能的影响,将A、B、C 3组不同含硫量的硫化矿粉尘云于20 L爆炸球中测定最小点火能。所得实验数据用点火概率(是否着火)表示最小点火能的方法,得出各组硫化矿粉尘云着火概率随点火能变化的分布曲线。通过实验得出各组硫化矿粉尘云最小点火能为:A500组3~4 k J、B500组6~8 k J、C500组>12 k J;A300组2~3 k J、B300组4~6 k J、C300组>12 k J;A200组2~3 k J、B200组6~8 k J、C200组>12 k J。结果表明:含硫量越高最小点火能越低,亦即爆炸危险性越大。展开更多
In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for t...In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for the original problem and design an extragradient thresholding algorithm (ETA) to solve the regularized model. Furthermore, we prove that any cluster point of the sequence generated by ETA is a solution of MCP. Finally, numerical experiments show that the ETA algorithm can effectively solve the l1 regularized projection minimization model and obtain the sparse solution of the mixed complementarity problem.展开更多
Consider the nonparametric regression model Y=go（T）+u,where Y is real-valued,u is a random error,T ranges over a nondegenerate compact interval,say[0,1],and go（·）is anunknown regression function,which is m（m...Consider the nonparametric regression model Y=go（T）+u,where Y is real-valued,u is a random error,T ranges over a nondegenerate compact interval,say[0,1],and go（·）is anunknown regression function,which is m（m≥0）times continuously differentiable and its ruthderivative,g<sub>0</sub><sup>（m）</sup>,satisfies a H■lder condition of order γ（m +γ】1/2）.A piecewise polynomial L<sub>1</sub>-norm estimator of go is proposed.Under some regularity conditions including that the randomerrors are independent but not necessarily have a common distribution,it is proved that therates of convergence of the piecewise polynomial L<sub>1</sub>-norm estimator are o(n<sup>-2（m+γ）+1/m+γ-1/δ</sup>almostsurely and o(n<sup>-2（m+γ）+1/m+γ-δ</sup>)in probability,which can arbitrarily approach the optimal rates ofconvergence for nonparametric regression,where δ is any number in （0, min（（m+γ-1/2）/3,γ））.展开更多
Truncated L1 regularization proposed by Fan in[5],is an approximation to the L0 regularization in high-dimensional sparse models.In this work,we prove the non-asymptotic error bound for the global optimal solution to ...Truncated L1 regularization proposed by Fan in[5],is an approximation to the L0 regularization in high-dimensional sparse models.In this work,we prove the non-asymptotic error bound for the global optimal solution to the truncated L1 regularized linear regression problem and study the support recovery property.Moreover,a primal dual active set algorithm(PDAS)for variable estimation and selection is proposed.Coupled with continuation by a warm-start strategy leads to a primal dual active set with continuation algorithm(PDASC).Data-driven parameter selection rules such as cross validation,BIC or voting method can be applied to select a proper regularization parameter.The application of the proposed method is demonstrated by applying it to simulation data and a breast cancer gene expression data set(bcTCGA).展开更多
文摘In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.
基金The research is supported by the National Natural Science Foundation of China (60574069)the Soft Science Foundation of Guangdong Province (2005B70101044)
文摘基于深圳综合索引的每周关门的价格，这篇文章用三个不同模型学习深圳证券市场的轻快：逻辑， AR (1 ) 和 AR (2 ) 。逻辑回归模型的时间变量参数被两个都使用弄平方法和时间变量参数评价方法的索引估计。并且 AR (1 ) 模特儿和 AR (2 ) 周刊的零平均数的系列当模特儿关门价格和它轻快率的零平均数的系列基于每周关门的价格的零平均数的系列的分析结果被建立。为错误预言的六个普通统计方法被用来测试预言的结果。这些方法是：吝啬的错误(我) ，吝啬的绝对错误(MAE ) ，根均方差(RMSE )*** 平均数绝对百分比错误(MAPE ) ， Akaike ‘ s 信息标准(AIC ) ，和贝叶斯的信息标准(BIC ) 。调查证明那个 AR (1 ) 模型展出最好的预言结果，而 AR (2 ) 模型展出预言在模型和逻辑回归建模的 AR (1 ) 之间是中间的结果。给词调音：逻辑回归模型；AR (1 ) 模型；AR (2 ) 模型；
基金supported by the National Natural Science Foundation of China(Grant No.61863010)the Key Research and Development Program of Shandong Province of China(Grant No.2019GGX101001)the Natural Science Foundation of Shandong Province of China(Grant No.ZR2018MC007)。
文摘Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in proteomics.In this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature subset.Finally,GTB-PPI model is constructed.Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,respectively.In addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling pathways.The results show that GTB-PPI can significantly improve accuracy of PPI prediction.The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.
文摘为探究含硫量对硫化矿粉尘云最小点火能的影响,将A、B、C 3组不同含硫量的硫化矿粉尘云于20 L爆炸球中测定最小点火能。所得实验数据用点火概率(是否着火)表示最小点火能的方法,得出各组硫化矿粉尘云着火概率随点火能变化的分布曲线。通过实验得出各组硫化矿粉尘云最小点火能为:A500组3~4 k J、B500组6~8 k J、C500组>12 k J;A300组2~3 k J、B300组4~6 k J、C300组>12 k J;A200组2~3 k J、B200组6~8 k J、C200组>12 k J。结果表明:含硫量越高最小点火能越低,亦即爆炸危险性越大。
文摘In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for the original problem and design an extragradient thresholding algorithm (ETA) to solve the regularized model. Furthermore, we prove that any cluster point of the sequence generated by ETA is a solution of MCP. Finally, numerical experiments show that the ETA algorithm can effectively solve the l1 regularized projection minimization model and obtain the sparse solution of the mixed complementarity problem.
基金Supported by the National Natural Science Foundation of China.
文摘Consider the nonparametric regression model Y=go（T）+u,where Y is real-valued,u is a random error,T ranges over a nondegenerate compact interval,say[0,1],and go（·）is anunknown regression function,which is m（m≥0）times continuously differentiable and its ruthderivative,g<sub>0</sub><sup>（m）</sup>,satisfies a H■lder condition of order γ（m +γ】1/2）.A piecewise polynomial L<sub>1</sub>-norm estimator of go is proposed.Under some regularity conditions including that the randomerrors are independent but not necessarily have a common distribution,it is proved that therates of convergence of the piecewise polynomial L<sub>1</sub>-norm estimator are o(n<sup>-2（m+γ）+1/m+γ-1/δ</sup>almostsurely and o(n<sup>-2（m+γ）+1/m+γ-δ</sup>)in probability,which can arbitrarily approach the optimal rates ofconvergence for nonparametric regression,where δ is any number in （0, min（（m+γ-1/2）/3,γ））.
文摘Truncated L1 regularization proposed by Fan in[5],is an approximation to the L0 regularization in high-dimensional sparse models.In this work,we prove the non-asymptotic error bound for the global optimal solution to the truncated L1 regularized linear regression problem and study the support recovery property.Moreover,a primal dual active set algorithm(PDAS)for variable estimation and selection is proposed.Coupled with continuation by a warm-start strategy leads to a primal dual active set with continuation algorithm(PDASC).Data-driven parameter selection rules such as cross validation,BIC or voting method can be applied to select a proper regularization parameter.The application of the proposed method is demonstrated by applying it to simulation data and a breast cancer gene expression data set(bcTCGA).
