The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We
This paper presents the comparison of the text document space dimension reduction and the text document clustering and also the keyword space dimension reduction and keyword clustering by the latent semantic analysis and by the Hebbian neural network with Oja learning rule. Results of this neural network are compared
approach. To generate the concept, keywords are extracted from the documents but the extracted set is very large. So for dimensionality reduction, SVD is applied. This paper proposes a novel approach for document clustering based on Formal Concept Analysis (FCA). Concept generation and dimensionality reduction are the two
With the number of registered Web services growing, Identifying desired Web service is crucial for Web users. Current keyword based service search are inefficient in two main aspects: poor scalability and lack of semantics. Firstly ,the users are overwhelmed by the huge number of irrelevant services returned. Secondly
With an ever-increasing number of Web services being available, finding desired Web service is crucial for service users. Current keyword search and most existing approaches are inefficient in two main aspects: poor scalability and lack of semantics. Firstly, users are overwhelmed by the huge number of irrelevant
The basic principle of Classic traditional information retrieval model is the machine matching of the key word, namely retrieval based on keywords. This paper proposes a pre-clustering-based latent semantic analysis algorithm for document retrieval. The algorithm can solve the problem of time consuming computation of
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.