How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paperpresents a new event detection method for the current new event det...How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paperpresents a new event detection method for the current new event detection system, based on sorted subtopic matching algorithm and constructs the entire design framework. In this p^per, the subtopics contained in old topics （or news stories） are sorted in descending order according to their importance to the topic（or news stories）, and form a sorted subtopic sequence. In the process of subtopic matching, subtopic scoring matrix is used to determine whether a new story is reporting a new event. Experimental results show that the sorted subtopic matching model improved the accuracy and effectiveness ofthenew event detection system in cyberspace.展开更多
In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are present...In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are presented. AMFNN combines additive inference and multiplicative inference into an integral whole, reasonably makes use of their advantages of inference and effectively overcomes their weaknesses when they are used for inference separately. Here, an error back propagation algorithm for AMFNN is presented based on the gradient descent method. Comparisons between the AMFNN and six representative fuzzy inference methods shows that the AMFNN is characterized by higher reasoning precision, wider application scope, stronger generalization capability and easier implementation.展开更多
基金Foundation item:Supported by national social science foundation of China(12ef119)Key projects in the science&technology prograrn of science and technology department of Tibet autonomous region(Z2013B28G28/02)National undergraduate training programs for innovation and entrepreneurship(201210694019)~~
基金Funded by the Planning Project of National Language Committee in the "12th 5-year Plan"(No.YB125-49)the Foundation for Key Program of Ministry of Education,China(No.212167)the Fundamental Research Funds for the Central Universities(No.SWJTU12CX096)
文摘How to quickly and accurately detect new topics from massive data online becomes a main problem of public opinion monitoring in cyberspace. This paperpresents a new event detection method for the current new event detection system, based on sorted subtopic matching algorithm and constructs the entire design framework. In this p^per, the subtopics contained in old topics （or news stories） are sorted in descending order according to their importance to the topic（or news stories）, and form a sorted subtopic sequence. In the process of subtopic matching, subtopic scoring matrix is used to determine whether a new story is reporting a new event. Experimental results show that the sorted subtopic matching model improved the accuracy and effectiveness ofthenew event detection system in cyberspace.
文摘In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are presented. AMFNN combines additive inference and multiplicative inference into an integral whole, reasonably makes use of their advantages of inference and effectively overcomes their weaknesses when they are used for inference separately. Here, an error back propagation algorithm for AMFNN is presented based on the gradient descent method. Comparisons between the AMFNN and six representative fuzzy inference methods shows that the AMFNN is characterized by higher reasoning precision, wider application scope, stronger generalization capability and easier implementation.
