AIM: To investigate the expression of markers that are correlated with the prognosis of colorectal cancer (CRC) patients. METHODS: One hundred and fifty-six CRC patientswere followed up for more than 3 years after rad...AIM: To investigate the expression of markers that are correlated with the prognosis of colorectal cancer (CRC) patients. METHODS: One hundred and fifty-six CRC patientswere followed up for more than 3 years after radical surgery. Immunohistochemical (IHC) analysis was performed to detect the expression of 14 pathway-related markers (p53, APC, p21ras, E-cadherin, endothelin-B receptor, Shp2, ADCY-2, SPARCL1, neuroligin1, hsp27, mmp-9, MAPK, MSH2 and rho) in specimens from these patients. Bioinformatics analysis involving a Support Vector Machine (SVM) was used to determine the best prognostic model from combinations of these markers. RESULTS: Seven markers (SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK) were significantly related to the prognosis and clinical pathological features of the CRC patients (P < 0.05). Prognostic models were established through SVM from combinations of these 7 markers and proved able to differentiate patients with dissimilar survival, especially in stage Ⅱ/Ⅲ patients. According to the best prognostic model, the p53/SPARCL1 model, patients having high p53 and low SPARCL1 expression had about 50% lower 3-year survival than others (P < 0.001). CONCLUSION: SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK are potential prognostic markers in CRC. A p53/SPARCL1 bioinformatics model may be used as a supplement to tumor-nodes-metastasis staging.展开更多
To increase accuracy of navigation parameters,a perspective measuring complex with intellectual components is developed.Conception of synthesis optimal structure of the measuring complex is realized basing on a select...To increase accuracy of navigation parameters,a perspective measuring complex with intellectual components is developed.Conception of synthesis optimal structure of the measuring complex is realized basing on a selective method using principles of the functional systems.Selection of measured information is finished by original numeric criterion of observation level of state vector components.Prediction is realized by algorithm of self-organization that makes synthesis of the optimal complication.Therefore mechanism of self-regulation is realized and accuracy of the selective navigation complex is increased.展开更多
In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of...In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.展开更多
The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, includ...The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, including the maximum water inrush, water inrush coefficient and water abundance in aquifers of working face, were processed by the statistical analysis. The analysis results indicate that both water inrush coefficient and water abundance in aquifers should be taken into consideration when evaluating the danger of water inrush from coal seam floor. The prediction model of safe-mining evaluation grade was built by using the support vector machine, and the result shows that this model has high classification accuracy. A feasible classification system of water-inrush safety evaluation can be got by using the data visualization method which makes the implicit support vector machine models explicit.展开更多
In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fu...In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.展开更多
Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed, thes...Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed, these only focus on accuracy which is based on the leave-one-out cross validation procedure. Information-retrieval-related performance measures are always neglected in a kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the leave-one-out procedure. Experiments have proven that our proposed estimators are both effective and stable.展开更多
A least squares version of the recently proposed weighted twin support vector machine with local information(WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algo...A least squares version of the recently proposed weighted twin support vector machine with local information(WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information(LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time.展开更多
文摘针对模拟电路故障诊断识别率较低的问题,提出了基于双空间特征提取的融合特权信息支持向量机的模拟电路故障诊断新方法。首先对采集的信号进行主成分分析(principal component analysis,PCA)——特征提取;并用融合特权信息支持向量机LUPI-SVM(SVM of learning using privileged information,LUPI-SVM)分类器和SVM-GA分类器进行预分类;对分类结果不同的样本进行独立成分分析(independent component analysis,ICA)—特征提取,并用LUPI_SVM进行分类识别,Sallen-Key滤波电路故障诊断仿真实验结果表明该方法有效提高了分类的性能,为模拟电路故障诊断提供了新的途径。
基金Supported by Grants from the Major State Basic Research Development Program of China, 973 Program No. 2004CB518707the Zhejiang Provincial Natural Science Foundation of China,No. R2090353the Fundamental Research Funds for the Central Universities, No. KYJD09007
文摘AIM: To investigate the expression of markers that are correlated with the prognosis of colorectal cancer (CRC) patients. METHODS: One hundred and fifty-six CRC patientswere followed up for more than 3 years after radical surgery. Immunohistochemical (IHC) analysis was performed to detect the expression of 14 pathway-related markers (p53, APC, p21ras, E-cadherin, endothelin-B receptor, Shp2, ADCY-2, SPARCL1, neuroligin1, hsp27, mmp-9, MAPK, MSH2 and rho) in specimens from these patients. Bioinformatics analysis involving a Support Vector Machine (SVM) was used to determine the best prognostic model from combinations of these markers. RESULTS: Seven markers (SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK) were significantly related to the prognosis and clinical pathological features of the CRC patients (P < 0.05). Prognostic models were established through SVM from combinations of these 7 markers and proved able to differentiate patients with dissimilar survival, especially in stage Ⅱ/Ⅲ patients. According to the best prognostic model, the p53/SPARCL1 model, patients having high p53 and low SPARCL1 expression had about 50% lower 3-year survival than others (P < 0.001). CONCLUSION: SPARCL1, Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK are potential prognostic markers in CRC. A p53/SPARCL1 bioinformatics model may be used as a supplement to tumor-nodes-metastasis staging.
文摘To increase accuracy of navigation parameters,a perspective measuring complex with intellectual components is developed.Conception of synthesis optimal structure of the measuring complex is realized basing on a selective method using principles of the functional systems.Selection of measured information is finished by original numeric criterion of observation level of state vector components.Prediction is realized by algorithm of self-organization that makes synthesis of the optimal complication.Therefore mechanism of self-regulation is realized and accuracy of the selective navigation complex is increased.
文摘In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.
基金Financial supports for this work, provided by National Natural Key Science Foundation of China (No. 50539080)Ministry of Education Research Fund for the doctoral program of China (No. 20133718110004)+2 种基金the Natural Science Key Foundation of Shandong Province of China (No. ZR2011EEZ002)the Technology Project Development Plan of Qingdao Economic and Technological Development Zone of China (No. 2013-1-62)SDUST Research Fund of China (No. 2012KYTD101)
文摘The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, including the maximum water inrush, water inrush coefficient and water abundance in aquifers of working face, were processed by the statistical analysis. The analysis results indicate that both water inrush coefficient and water abundance in aquifers should be taken into consideration when evaluating the danger of water inrush from coal seam floor. The prediction model of safe-mining evaluation grade was built by using the support vector machine, and the result shows that this model has high classification accuracy. A feasible classification system of water-inrush safety evaluation can be got by using the data visualization method which makes the implicit support vector machine models explicit.
基金National Natural Science Foundation of China(No.61663021)Science and Technology Support Project of Gansu Province(No.1304GKCA023)Scientific Research Project in University of Gansu Province(No.2017A-025)
文摘In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.
文摘Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed, these only focus on accuracy which is based on the leave-one-out cross validation procedure. Information-retrieval-related performance measures are always neglected in a kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the leave-one-out procedure. Experiments have proven that our proposed estimators are both effective and stable.
基金Project(61105057)supported by the National Natural Science Foundation of ChinaProject(13KJB520024)supported by the Natural Science Foundation of Jiangsu Higher Education Institutes of ChinaProject supported by Jiangsu Province Qing Lan Project,China
文摘A least squares version of the recently proposed weighted twin support vector machine with local information(WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information(LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time.
