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
The problem of "rich topics get richer"(RTGR) is popular to the topic models,which will bring the wrong topic distribution if the distributing process has not been intervened.In standard LDA(Latent Dirichlet...The problem of "rich topics get richer"(RTGR) is popular to the topic models,which will bring the wrong topic distribution if the distributing process has not been intervened.In standard LDA(Latent Dirichlet Allocation) model,each word in all the documents has the same statistical ability.In fact,the words have different impact towards different topics.Under the guidance of this thought,we extend ILDA(Infinite LDA) by considering the bias role of words to divide the topics.We propose a self-adaptive topic model to overcome the RTGR problem specifically.The model proposed in this paper is adapted to three questions:(1) the topic number is changeable with the collection of the documents,which is suitable for the dynamic data;(2) the words have discriminating attributes to topic distribution;(3) a selfadaptive method is used to realize the automatic re-sampling.To verify our model,we design a topic evolution analysis system which can realize the following functions:the topic classification in each cycle,the topic correlation in the adjacent cycles and the strength calculation of the sub topics in the order.The experiment both on NIPS corpus and our self-built news collections showed that the system could meet the given demand,the result was feasible.展开更多
In the recent informatization of Chinese courts, the huge amount of law cases and judgment documents, which were digital stored,has provided a good foundation for the research of judicial big data and machine learning...In the recent informatization of Chinese courts, the huge amount of law cases and judgment documents, which were digital stored,has provided a good foundation for the research of judicial big data and machine learning. In this situation, some ideas about Chinese courts can reach automation or get better result through the research of machine learning, such as similar documents recommendation, workload evaluation based on similarity of judgement documents and prediction of possible relevant statutes. In trying to achieve all above mentioned, and also in face of the characteristics of Chinese judgement document, we propose a topic model based approach to measure the text similarity of Chinese judgement document, which is based on TF-IDF, Latent Dirichlet Allocation (LDA), Labeled Latent Dirichlet Allocation (LLDA) and other treatments. Combining with the characteristics of Chinese judgment document,we focus on the specific steps of approach, the preprocessing of corpus, the parameters choices of training and the evaluation of similarity measure result. Besides, implementing the approach for prediction of possible statutes and regarding the prediction accuracy as the evaluation metric, we designed experiments to demonstrate the reasonability of decisions in the process of design and the high performance of our approach on text similarity measure. The experiments also show the restriction of our approach which need to be focused in future work.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.
基金ACKNOWLEDGMENTS This work is supported by grants National 973 project （No.2013CB29606）, Natural Science Foundation of China （No.61202244）, research fund of ShangQiu Normal Colledge （No. 2013GGJS013）. N1PS corpus is supported by SourceForge. We thank the anonymous reviewers for their helpful comments.
文摘The problem of "rich topics get richer"(RTGR) is popular to the topic models,which will bring the wrong topic distribution if the distributing process has not been intervened.In standard LDA(Latent Dirichlet Allocation) model,each word in all the documents has the same statistical ability.In fact,the words have different impact towards different topics.Under the guidance of this thought,we extend ILDA(Infinite LDA) by considering the bias role of words to divide the topics.We propose a self-adaptive topic model to overcome the RTGR problem specifically.The model proposed in this paper is adapted to three questions:(1) the topic number is changeable with the collection of the documents,which is suitable for the dynamic data;(2) the words have discriminating attributes to topic distribution;(3) a selfadaptive method is used to realize the automatic re-sampling.To verify our model,we design a topic evolution analysis system which can realize the following functions:the topic classification in each cycle,the topic correlation in the adjacent cycles and the strength calculation of the sub topics in the order.The experiment both on NIPS corpus and our self-built news collections showed that the system could meet the given demand,the result was feasible.
文摘In the recent informatization of Chinese courts, the huge amount of law cases and judgment documents, which were digital stored,has provided a good foundation for the research of judicial big data and machine learning. In this situation, some ideas about Chinese courts can reach automation or get better result through the research of machine learning, such as similar documents recommendation, workload evaluation based on similarity of judgement documents and prediction of possible relevant statutes. In trying to achieve all above mentioned, and also in face of the characteristics of Chinese judgement document, we propose a topic model based approach to measure the text similarity of Chinese judgement document, which is based on TF-IDF, Latent Dirichlet Allocation (LDA), Labeled Latent Dirichlet Allocation (LLDA) and other treatments. Combining with the characteristics of Chinese judgment document,we focus on the specific steps of approach, the preprocessing of corpus, the parameters choices of training and the evaluation of similarity measure result. Besides, implementing the approach for prediction of possible statutes and regarding the prediction accuracy as the evaluation metric, we designed experiments to demonstrate the reasonability of decisions in the process of design and the high performance of our approach on text similarity measure. The experiments also show the restriction of our approach which need to be focused in future work.
