To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms....To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms.Develop a semi-supervised document clustering approach based on the latent Dirichlet allocation(LDA)model,namely,pLDA,guided by the user provided key terms.Propose a generalized Polya urn(GPU) model to integrate the user preferences to the document clustering process.A Gibbs sampler was investigated to infer the document collection structure.Experiments on real datasets were taken to explore the performance of pLDA.The results demonstrate that the pLDA approach is effective.展开更多
As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability throug...As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability through unsupervised feature selection.Theoretical analysis shows that the discrimination capability of a topic is limited by the discrimination capability of its representative words.The discrimination capability of a word is approximated by the Information Gain of the word for topics,which is used to distinguish between "general word" and "special word" in LDA topics.Therefore,we add a constraint to the LDA objective function to let the "general words" only happen in "general topics" other than "special topics".Then a heuristic algorithm is presented to get the solution.Experiments show that this method can not only improve the information gain of topics,but also make the topics easier to understand by human.展开更多
The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system(IDS).IDS are consi...The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system(IDS).IDS are considered as an essential factor to fulfill security requirements.Recently,there are diverse Machine Learning(ML)approaches that are used for modeling effectual IDS.Most IDS are based on ML techniques and categorized as supervised and unsupervised.However,IDS with supervised learning is based on labeled data.This is considered as a common drawback and it fails to identify the attack patterns.Similarly,unsupervised learning fails to provide satisfactory outcomes.Therefore,this work concentrates on semi-supervised learning model known as Fuzzy based semi-supervised approach through Latent Dirichlet Allocation(F-LDA)for intrusion detection in cloud system.This helps to resolve the aforementioned challenges.Initially,LDA gives better generalization ability for training the labeled data.Similarly,to handle the unlabelled data,Fuzzy model has been adopted for analyzing the dataset.Here,preprocessing has been carried out to eliminate data redundancy over network dataset.In order to validate the efficiency of F-LDA towards ID,this model is tested under NSL-KDD cup dataset is a common traffic dataset.Simulation is done inMATLAB environment and gives better accuracy while comparing with benchmark standard dataset.The proposed F-LDAgives better accuracy and promising outcomes than the prevailing approaches.展开更多
基金National Natural Science Foundations of China(Nos.61262006,61462011,61202089)the Major Applied Basic Research Program of Guizhou Province Project,China(No.JZ20142001)+2 种基金the Science and Technology Foundation of Guizhou Province Project,China(No.LH20147636)the National Research Foundation for the Doctoral Program of Higher Education of China(No.20125201120006)the Graduate Innovated Foundations of Guizhou University Project,China(No.2015012)
文摘To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms.Develop a semi-supervised document clustering approach based on the latent Dirichlet allocation(LDA)model,namely,pLDA,guided by the user provided key terms.Propose a generalized Polya urn(GPU) model to integrate the user preferences to the document clustering process.A Gibbs sampler was investigated to infer the document collection structure.Experiments on real datasets were taken to explore the performance of pLDA.The results demonstrate that the pLDA approach is effective.
基金supported by National Nature Science Foundation of China under Grant No.60905017,61072061National High Technical Research and Development Program of China(863 Program)under Grant No.2009AA01A346+1 种基金111 Project of China under Grant No.B08004the Special Project for Innovative Young Researchers of Beijing University of Posts and Telecommunications
文摘As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability through unsupervised feature selection.Theoretical analysis shows that the discrimination capability of a topic is limited by the discrimination capability of its representative words.The discrimination capability of a word is approximated by the Information Gain of the word for topics,which is used to distinguish between "general word" and "special word" in LDA topics.Therefore,we add a constraint to the LDA objective function to let the "general words" only happen in "general topics" other than "special topics".Then a heuristic algorithm is presented to get the solution.Experiments show that this method can not only improve the information gain of topics,but also make the topics easier to understand by human.
文摘The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system(IDS).IDS are considered as an essential factor to fulfill security requirements.Recently,there are diverse Machine Learning(ML)approaches that are used for modeling effectual IDS.Most IDS are based on ML techniques and categorized as supervised and unsupervised.However,IDS with supervised learning is based on labeled data.This is considered as a common drawback and it fails to identify the attack patterns.Similarly,unsupervised learning fails to provide satisfactory outcomes.Therefore,this work concentrates on semi-supervised learning model known as Fuzzy based semi-supervised approach through Latent Dirichlet Allocation(F-LDA)for intrusion detection in cloud system.This helps to resolve the aforementioned challenges.Initially,LDA gives better generalization ability for training the labeled data.Similarly,to handle the unlabelled data,Fuzzy model has been adopted for analyzing the dataset.Here,preprocessing has been carried out to eliminate data redundancy over network dataset.In order to validate the efficiency of F-LDA towards ID,this model is tested under NSL-KDD cup dataset is a common traffic dataset.Simulation is done inMATLAB environment and gives better accuracy while comparing with benchmark standard dataset.The proposed F-LDAgives better accuracy and promising outcomes than the prevailing approaches.
