In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets w...In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.展开更多
Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP...Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.展开更多
AIM:To assess the application of the Kasai procedure in the surgical management of hilar bile duct strictures.METHODS:Ten consecutive patients between 2005 and 2011 with hilar bile duct strictures who underwent the Ka...AIM:To assess the application of the Kasai procedure in the surgical management of hilar bile duct strictures.METHODS:Ten consecutive patients between 2005 and 2011 with hilar bile duct strictures who underwent the Kasai procedure were retrospectively analyzed.Kasai portoenterostomy with the placement of biliary stents was performed in all patients.Clinical characteristics,postoperative complications,and long-term outcomes were analyzed.All patients were followed up for 2-60 mo postoperatively.RESULTS:Patients were classified according to the Bismuth classification of biliary strictures.There were two Bismuth Ⅲ and eight Bismuth Ⅳ lesions.Six lesions were benign and four were malignant.Of the benign lesions,three were due to post-cholecystectomy injury,one to trauma,one to inflammation,and one to inflammatory pseudotumor.Of the malignant lesions,four were due to hilar cholangiocarcinoma.All patients underwent Kasai portoenterostomy with the placement of biliary stents.There were no perioperative deaths.One patient experienced anastomotic leak and was managed conservatively.No other complications occurred perioperatively.During the follow-up period,all patients reported a good quality of life.CONCLUSION:The Kasai procedure combined with biliary stents may be appropriate for patients with hilar biliary stricture that cannot be managed by standard surgical methods.展开更多
A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the pr...A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.展开更多
Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were ...Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.展开更多
Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier (SVC) method in the estimation. Using forward and nonlinear inverse techniques, de...Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier (SVC) method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection (FS) algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data.展开更多
Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant informa...Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant information, we propose a new simple feature selection method, which can effectively filter the redundant features. First, to calculate the relationship between two words, the definitions of word frequency based relevance and correlative redundancy are introduced. Furthermore, an optimal feature selection(OFS) method is chosen to obtain a feature subset FS1. Finally, to improve the execution speed, the redundant features in FS1 are filtered by combining a predetermined threshold, and the filtered features are memorized in the linked lists. Experiments are carried out on three datasets(Web KB, 20-Newsgroups, and Reuters-21578) where in support vector machines and na?ve Bayes are used. The results show that the classification accuracy of the proposed method is generally higher than that of typical traditional methods(information gain, improved Gini index, and improved comprehensively measured feature selection) and the OFS methods. Moreover, the proposed method runs faster than typical mutual information-based methods(improved and normalized mutual information-based feature selections, and multilabel feature selection based on maximum dependency and minimum redundancy) while simultaneously ensuring classification accuracy. Statistical results validate the effectiveness of the proposed method in handling redundant information in text classification.展开更多
Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping...Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling.As area classes are rarely completely separable in empirically realized discriminant space,where class inseparabil-ity becomes more complicated for change categorization,we seek to quantify uncertainty in area classes(and change classes)due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively.Experiments using real datasets were carried out,and a Bayesian method was used to obtain change maps.We found that there are large differences be-tween uncertainty statistics referring to data classes and information classes.Therefore,uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis,enabling quanti-fication of uncertainty due to partially random measurement errors,and systematic categorical discrepancies,respectively.展开更多
基金The Program for New Century Excellent Talents in University(No.NCET-11-0565)the Fundamental Research Funds for the Central Universities(No.K13JB00160,2012JBZ010,2011JBM217)+2 种基金the Ph.D.Programs Foundation of Ministry of Education of China(No.20120009120010)the Program for Innovative Research Team in University of Ministry of Education of China(No.IRT201206)the Natural Science Foundation of Shandong Province(No.ZR2012FM010,ZR2011FZ001)
文摘In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.
基金Projects(60903082,60975042)supported by the National Natural Science Foundation of ChinaProject(20070217043)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.
文摘AIM:To assess the application of the Kasai procedure in the surgical management of hilar bile duct strictures.METHODS:Ten consecutive patients between 2005 and 2011 with hilar bile duct strictures who underwent the Kasai procedure were retrospectively analyzed.Kasai portoenterostomy with the placement of biliary stents was performed in all patients.Clinical characteristics,postoperative complications,and long-term outcomes were analyzed.All patients were followed up for 2-60 mo postoperatively.RESULTS:Patients were classified according to the Bismuth classification of biliary strictures.There were two Bismuth Ⅲ and eight Bismuth Ⅳ lesions.Six lesions were benign and four were malignant.Of the benign lesions,three were due to post-cholecystectomy injury,one to trauma,one to inflammation,and one to inflammatory pseudotumor.Of the malignant lesions,four were due to hilar cholangiocarcinoma.All patients underwent Kasai portoenterostomy with the placement of biliary stents.There were no perioperative deaths.One patient experienced anastomotic leak and was managed conservatively.No other complications occurred perioperatively.During the follow-up period,all patients reported a good quality of life.CONCLUSION:The Kasai procedure combined with biliary stents may be appropriate for patients with hilar biliary stricture that cannot be managed by standard surgical methods.
基金Supported by the Major State Basic Research Development Program of China under Grant No. 5132103ZZT32.
文摘A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.
基金Project(61072087)supported by the National Natural Science Foundation of ChinaProject(2010011020-1)supported by the Natural Scientific Foundation of Shanxi Province,ChinaProject(20093010)supported by Graduate Innovation Fundation of Shanxi Province,China
文摘Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.
文摘Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier (SVC) method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection (FS) algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data.
基金Project supported by the Joint Funds of the National Natural Science Foundation of China(No.U1509214)the Beijing Natural Science Foundation,China(No.4174105)+1 种基金the Key Projects of National Bureau of Statistics of China(No.2017LZ05)the Discipline Construction Foundation of the Central University of Finance and Economics,China(No.2016XX02)
文摘Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant information, we propose a new simple feature selection method, which can effectively filter the redundant features. First, to calculate the relationship between two words, the definitions of word frequency based relevance and correlative redundancy are introduced. Furthermore, an optimal feature selection(OFS) method is chosen to obtain a feature subset FS1. Finally, to improve the execution speed, the redundant features in FS1 are filtered by combining a predetermined threshold, and the filtered features are memorized in the linked lists. Experiments are carried out on three datasets(Web KB, 20-Newsgroups, and Reuters-21578) where in support vector machines and na?ve Bayes are used. The results show that the classification accuracy of the proposed method is generally higher than that of typical traditional methods(information gain, improved Gini index, and improved comprehensively measured feature selection) and the OFS methods. Moreover, the proposed method runs faster than typical mutual information-based methods(improved and normalized mutual information-based feature selections, and multilabel feature selection based on maximum dependency and minimum redundancy) while simultaneously ensuring classification accuracy. Statistical results validate the effectiveness of the proposed method in handling redundant information in text classification.
基金Supported by the National Natural Science Foundation of China (No.41171346,No. 41071286)the Fundamental Research Funds for the Central Universities (No. 20102130103000005)the National 973 Program of China (No. 2007CB714402‐5)
文摘Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling.As area classes are rarely completely separable in empirically realized discriminant space,where class inseparabil-ity becomes more complicated for change categorization,we seek to quantify uncertainty in area classes(and change classes)due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively.Experiments using real datasets were carried out,and a Bayesian method was used to obtain change maps.We found that there are large differences be-tween uncertainty statistics referring to data classes and information classes.Therefore,uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis,enabling quanti-fication of uncertainty due to partially random measurement errors,and systematic categorical discrepancies,respectively.