Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580851
Wei Xu, Zhen-Wen Han, Jian Ma
Influenza epidemics detection is critically important in recent years because there is a significant economic and public health impact associated with the influenza epidemic. Influenza epidemics detection attracts much attention from governments, organizations, and research institutes, and recently, a novel method using search engine query data to detect influenza activities was presented by Google. In this paper, a data mining based framework using web data is introduced for influenza epidemics detection. Under the framework, a neural network based approach using search engine query data is developed to detect influenza activities. In the proposed method, an automated feature selection model is firstly constructed to reduce the dimension of the query data. Secondly, various neural networks are employed to model the relationship between influenza-like illness data and query data. Thirdly, an optimal neural network is selected as the detector by using the cross-validation method. Finally, the selective neural network detector with the best feature subset is used to detect influenza activities. Experimental results show that the proposed method can outperform traditional statistical models and other models used in the experiments in terms of accuracy. These findings imply that data mining, such as neural network method, can be used as a promising tool to detect influenza activities.
{"title":"A neural netwok based approach to detect influenza epidemics using search engine query data","authors":"Wei Xu, Zhen-Wen Han, Jian Ma","doi":"10.1109/ICMLC.2010.5580851","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580851","url":null,"abstract":"Influenza epidemics detection is critically important in recent years because there is a significant economic and public health impact associated with the influenza epidemic. Influenza epidemics detection attracts much attention from governments, organizations, and research institutes, and recently, a novel method using search engine query data to detect influenza activities was presented by Google. In this paper, a data mining based framework using web data is introduced for influenza epidemics detection. Under the framework, a neural network based approach using search engine query data is developed to detect influenza activities. In the proposed method, an automated feature selection model is firstly constructed to reduce the dimension of the query data. Secondly, various neural networks are employed to model the relationship between influenza-like illness data and query data. Thirdly, an optimal neural network is selected as the detector by using the cross-validation method. Finally, the selective neural network detector with the best feature subset is used to detect influenza activities. Experimental results show that the proposed method can outperform traditional statistical models and other models used in the experiments in terms of accuracy. These findings imply that data mining, such as neural network method, can be used as a promising tool to detect influenza activities.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116925051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The large-sized coal-fired power units characterizes as wide thermodynamic scale, huge equipment, large flow and mass, which results in distinct nonlinear feature in energy transmission, conversion and dissipation for specific equipment, system and process. There's highly coupling and nonlinear correlation between the energy consumption in power generation and the external environment, resources and load demand. A data mining-based modeling methodology for complex system was proposed in this paper, reflecting the influences of boundary constraints and implementing the reconstruction of operation states. Based on this, a Spatial-temporal Distribution Model of Energy Consumption at Overall Conditions (SDMEC) for large coal-fired power units was built based on ε-SVR data mining and verified by the practical operation data of thermal power units. The result shows that the ε-SVR-based model is easy to implement and explicit to interpret with high accuracy.
{"title":"Data mining-based modeling and application in the energy-saving analysis of large coal-fired power units","authors":"Yongping Yang, Ning-Ling Wang, Zhi-Wei Zhang, De-gang Chen","doi":"10.1109/ICMLC.2010.5580941","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580941","url":null,"abstract":"The large-sized coal-fired power units characterizes as wide thermodynamic scale, huge equipment, large flow and mass, which results in distinct nonlinear feature in energy transmission, conversion and dissipation for specific equipment, system and process. There's highly coupling and nonlinear correlation between the energy consumption in power generation and the external environment, resources and load demand. A data mining-based modeling methodology for complex system was proposed in this paper, reflecting the influences of boundary constraints and implementing the reconstruction of operation states. Based on this, a Spatial-temporal Distribution Model of Energy Consumption at Overall Conditions (SDMEC) for large coal-fired power units was built based on ε-SVR data mining and verified by the practical operation data of thermal power units. The result shows that the ε-SVR-based model is easy to implement and explicit to interpret with high accuracy.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116244575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580665
Y. Qi, Jiyuan Liu, Huading Shi, D. Zhuang, Yunfeng Hu
Wind erosion is one of the major environmental problems in semi-arid and arid regions. Here we established a transect from northwest (Tariat, Mongolia) to southeast (Xilingol, Inner Mongolia of China) across the Mongolian Plateau, and estimated the soil wind erosion gradient patterns by using the 137Cs tracing technique. In the Mongolia section, the wind erosion rate increased gradually with vegetation type and climatic regimes, controlled by physical factors such as annual precipitation and vegetation coverage, etc. While in the Inner Mongolia section, the wind erosion rates were thrice as much as those of Bayannur of Mongolia. Besides the physical factors, higher population density and livestock carrying level should be responsible for the higher wind erosion rates in Inner Mongolia.
{"title":"Wind erosion gradient patterns of Mongolian Plateau","authors":"Y. Qi, Jiyuan Liu, Huading Shi, D. Zhuang, Yunfeng Hu","doi":"10.1109/ICMLC.2010.5580665","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580665","url":null,"abstract":"Wind erosion is one of the major environmental problems in semi-arid and arid regions. Here we established a transect from northwest (Tariat, Mongolia) to southeast (Xilingol, Inner Mongolia of China) across the Mongolian Plateau, and estimated the soil wind erosion gradient patterns by using the 137Cs tracing technique. In the Mongolia section, the wind erosion rate increased gradually with vegetation type and climatic regimes, controlled by physical factors such as annual precipitation and vegetation coverage, etc. While in the Inner Mongolia section, the wind erosion rates were thrice as much as those of Bayannur of Mongolia. Besides the physical factors, higher population density and livestock carrying level should be responsible for the higher wind erosion rates in Inner Mongolia.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"507 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116330668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580721
Xi Yang, Hong Yan
The deformation mechanism of double helical DNA can be elucidated by the correlations between various conformational properties of its constituent dinucleotides. In this paper, we study the structural features of nucleosomes by performing principle component analysis on structural parameters within three categories: sugar-phosphate backbone, base pairs and base steps. The aim of this study is to find out the coupling modes of these conformational properties which are probably responsible for the special conformational settings of dinucleotides in nucleosomes. For comparison, a number of B-form oligomers are selected and subjected to PCA. Analysis of similarity and difference between nucleosomes and oligomers is implemented. The result shows that nucleosomes have a series of unique coupling patterns and reveals the multidimension-coordinated deformation mechanisms.
{"title":"Principal component analysis of nucleosome DNA conformational data","authors":"Xi Yang, Hong Yan","doi":"10.1109/ICMLC.2010.5580721","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580721","url":null,"abstract":"The deformation mechanism of double helical DNA can be elucidated by the correlations between various conformational properties of its constituent dinucleotides. In this paper, we study the structural features of nucleosomes by performing principle component analysis on structural parameters within three categories: sugar-phosphate backbone, base pairs and base steps. The aim of this study is to find out the coupling modes of these conformational properties which are probably responsible for the special conformational settings of dinucleotides in nucleosomes. For comparison, a number of B-form oligomers are selected and subjected to PCA. Analysis of similarity and difference between nucleosomes and oligomers is implemented. The result shows that nucleosomes have a series of unique coupling patterns and reveals the multidimension-coordinated deformation mechanisms.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116792847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580766
Z. Xuan, Wang Yan
In this paper, the method of threshold segmentation is introduced, which focus on the image characteristics of the sperm video, base on the dynamic threshold and combine with region growing arithmetic. The method is based on the movement characteristics and the brightness characteristics of the objective sperm to distinct, and then uses the region growing algorithm to calculate the sperm region, finally according to this gray area to determine the threshold. The results show that this method has better performance to divide the sperm goal.
{"title":"The sperm video segmentation based on dynamic threshold","authors":"Z. Xuan, Wang Yan","doi":"10.1109/ICMLC.2010.5580766","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580766","url":null,"abstract":"In this paper, the method of threshold segmentation is introduced, which focus on the image characteristics of the sperm video, base on the dynamic threshold and combine with region growing arithmetic. The method is based on the movement characteristics and the brightness characteristics of the objective sperm to distinct, and then uses the region growing algorithm to calculate the sperm region, finally according to this gray area to determine the threshold. The results show that this method has better performance to divide the sperm goal.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116853209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580807
Ching-Hung Lee, Hsin-Wei Chiu, Chung-Ta Li
In this paper, we propose a novel neuro-fuzzy classification system by a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP). The neuro-fuzzy classification system is constructed by an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS). The hybrid algorithm SEMBP combines the advantages of EM and BP algorithms. Three classification problems: the XOR data set, the breast cancer data set, and the Iris data set are used to illustrate the performance of our approach.
{"title":"A novel neuro-fuzzy classification system design by a species-based hybrid algorithm","authors":"Ching-Hung Lee, Hsin-Wei Chiu, Chung-Ta Li","doi":"10.1109/ICMLC.2010.5580807","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580807","url":null,"abstract":"In this paper, we propose a novel neuro-fuzzy classification system by a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP). The neuro-fuzzy classification system is constructed by an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS). The hybrid algorithm SEMBP combines the advantages of EM and BP algorithms. Three classification problems: the XOR data set, the breast cancer data set, and the Iris data set are used to illustrate the performance of our approach.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580501
Weifeng Zhang, Chao-Chun Liu, Hong Yan
Gene temporal expression data clustering has been widely used to study dynamic biological systems. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. To improve the clustering performance, we introduce a novel clustering approach based on the continuous representations and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure to take into account the temporal information and the relative changes of time series. Experimental results show that the proposed method is robust to noise and can produce meaningful clustering results.
{"title":"Gene time series data clustering based on continuous representations and an energy based similarity measure","authors":"Weifeng Zhang, Chao-Chun Liu, Hong Yan","doi":"10.1109/ICMLC.2010.5580501","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580501","url":null,"abstract":"Gene temporal expression data clustering has been widely used to study dynamic biological systems. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. To improve the clustering performance, we introduce a novel clustering approach based on the continuous representations and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure to take into account the temporal information and the relative changes of time series. Experimental results show that the proposed method is robust to noise and can produce meaningful clustering results.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125814857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580769
Xiao-yong Li, Lidai Zhou, Yong Shi, Yu Guo
The main difference between cloud computing and traditional enterprise internal IT services is that the owner and the user of cloud IT infrastructures are separated in cloud. This change requires a security duty separation in cloud computing. Cloud service providers (CSP) should secure the services they offer and cannot exceed the customers' authorities. Currently, no traditional information security products can meet this requirement. A multi-tenancy trusted computing environment model (MTCEM) is designed for IaaS delivery model, and its purpose is to assure a trusted cloud infrastructure to customers. MTCEM presents a dual level transitive trust mechanism and supports a security duty separation function simultaneously. With MTCEM, CSP and customers can cooperate to build and maintain a trusted cloud computing environment. MTCEM can be used to improve customers' confidence on cloud computing. The prototype of MTCEM shows that it has low impact on system performance and it is technically and practically feasible.
{"title":"A trusted computing environment model in cloud architecture","authors":"Xiao-yong Li, Lidai Zhou, Yong Shi, Yu Guo","doi":"10.1109/ICMLC.2010.5580769","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580769","url":null,"abstract":"The main difference between cloud computing and traditional enterprise internal IT services is that the owner and the user of cloud IT infrastructures are separated in cloud. This change requires a security duty separation in cloud computing. Cloud service providers (CSP) should secure the services they offer and cannot exceed the customers' authorities. Currently, no traditional information security products can meet this requirement. A multi-tenancy trusted computing environment model (MTCEM) is designed for IaaS delivery model, and its purpose is to assure a trusted cloud infrastructure to customers. MTCEM presents a dual level transitive trust mechanism and supports a security duty separation function simultaneously. With MTCEM, CSP and customers can cooperate to build and maintain a trusted cloud computing environment. MTCEM can be used to improve customers' confidence on cloud computing. The prototype of MTCEM shows that it has low impact on system performance and it is technically and practically feasible.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125899236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580677
Xiangying Dai, Qingcai Chen, Xiaolong Wang, Jun Xu
In this paper, we apply TDT technology to the vertical search engine in the financial field. The returned results are grouped into several topics with the stock as the unit. Then we show the topics to the users in time series order. As a result, users can easily learn about the important events which belong to a stock. Moreover, the causes and the effects of these events can also be found out easily. We improve the common agglomerative hierarchical clustering algorithm based on average-link method, which is then used to implement the retrospective topic detection and the online topic detection of news stories of the stocks. Additionally, the improved single pass clustering algorithm is employed to accomplish topic tracking. We consider that the feature terms which occur in the title of a news story contribute more during the similarity calculation and increase their corresponding weights. Experiments are performed on two datasets which are annotated by human judgment. The results show that the proposed method can effectively detect and track the online financial topics.
{"title":"Online topic detection and tracking of financial news based on hierarchical clustering","authors":"Xiangying Dai, Qingcai Chen, Xiaolong Wang, Jun Xu","doi":"10.1109/ICMLC.2010.5580677","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580677","url":null,"abstract":"In this paper, we apply TDT technology to the vertical search engine in the financial field. The returned results are grouped into several topics with the stock as the unit. Then we show the topics to the users in time series order. As a result, users can easily learn about the important events which belong to a stock. Moreover, the causes and the effects of these events can also be found out easily. We improve the common agglomerative hierarchical clustering algorithm based on average-link method, which is then used to implement the retrospective topic detection and the online topic detection of news stories of the stocks. Additionally, the improved single pass clustering algorithm is employed to accomplish topic tracking. We consider that the feature terms which occur in the title of a news story contribute more during the similarity calculation and increase their corresponding weights. Experiments are performed on two datasets which are annotated by human judgment. The results show that the proposed method can effectively detect and track the online financial topics.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580925
Hai-Lan Ding, Wing W. Y. Ng, P. Chan, Dong-Liang Wu, Xiao-Ling Chen, D. Yeung
As pervasive computing becomes more popular, the importance of context-aware applications increases. Physical location of user is important to context-aware pervasive application providers. RFID is one of the most widely adopted wireless positioning technologies. Compared to other wireless technologies, e.g. GPS and WLAN, RFID is particularly suitable for indoor positioning. Existing methods usually assume a constant environment for the application field. However, this may not be true in many cases. For example, warehouse may have different goods yielding different interference to RFID signal in different days. This paper proposes a new method to estimate locations of objects based on RFID. The indoor positioning with RFID reader based on the received signal strength and passive UHF tags as reference tags. A Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM) is adopted to learn the object location based on received RFID signals. The L-GEM provides an estimate on the generalization capability of the RBFNN which is important to locate future unseen samples correctly in different yet similar environments. Simulation experiments show that the proposed method outperforms existing RFID based indoor positioning method.
{"title":"RFID indoor positioning using RBFNN with L-GEM","authors":"Hai-Lan Ding, Wing W. Y. Ng, P. Chan, Dong-Liang Wu, Xiao-Ling Chen, D. Yeung","doi":"10.1109/ICMLC.2010.5580925","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580925","url":null,"abstract":"As pervasive computing becomes more popular, the importance of context-aware applications increases. Physical location of user is important to context-aware pervasive application providers. RFID is one of the most widely adopted wireless positioning technologies. Compared to other wireless technologies, e.g. GPS and WLAN, RFID is particularly suitable for indoor positioning. Existing methods usually assume a constant environment for the application field. However, this may not be true in many cases. For example, warehouse may have different goods yielding different interference to RFID signal in different days. This paper proposes a new method to estimate locations of objects based on RFID. The indoor positioning with RFID reader based on the received signal strength and passive UHF tags as reference tags. A Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM) is adopted to learn the object location based on received RFID signals. The L-GEM provides an estimate on the generalization capability of the RBFNN which is important to locate future unseen samples correctly in different yet similar environments. Simulation experiments show that the proposed method outperforms existing RFID based indoor positioning method.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124823042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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