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.
Traditional automatic classifiers often conduct misclassifications. Folksonomy, a new manual classification scheme based on tagging efforts of users with freely chosen keywords can effective resolve this problem. Even though the scalability of folksonomy is much higher than the other manual classification schemes, the
With the dramatic increasing number of available Web services, how to locate the right services is becoming a big challenge in pervasive environments. The Web services discovery mechanism of UDDI based on keywords and simple classification can not meet the current needs. A semantic distance between ontology concepts
Traditional text learning algorithms need labeled documents to supervise the learning process, but labeling documents of a specific class is often expensive and time consuming. We observe it is convenient to use some keywords(i.e. class-descriptions) to describe class sometimes. However, short class-description
EMMA is an e-mail management assistant based on ripple down rules, providing a high degree of classification accuracy while simplifying the task of maintaining the consistency of the rule base. A naive Bayes algorithm is used to improve the usability of EMMA by suggesting keywords to help the user define rules. In
Traditional information retrieval (IR) method use keywords matching to filter the documents, but usually retrieves unrelated Web pages. In order to effectively classify Web pages, we present a Web page categorization algorithm, named WebPSC (Web page similarity categorization). This algorithm uses latent semantic
obtain latent semantic structure of original term-document matrix solving the polysemous and synonymous keywords problem. LS-SVM is an effective method for learning the classification knowledge from massive data, especially on condition of high cost in getting labeled classical examples. We adopt a novel method of Web page
With rapid development of Internet information, It is quite an important project for data mining that how to classify these large amounts of texts. In this paper, we propose an improved text classify cluster algorithm, while calculating similarity, we synthetically consider the relationship between keywords and
Through the analysis of the information on the contents of the document which contained in title, abstract and keywords, find out which documents are more relativity with user's retrieval expectation, this paper adopted "document retrieval expected value" as be the indicator, builds the mathematical model for it, and
In order to enable more effective image retrieval via keywords, automatic image annotation and categorization becomes an important problem in computer vision and content based image retrieval. Unfortunately, there exists a semantic gap between the low-level feature vectors and the high-level semantics or concepts. In
filtering recommendation is implemented using intelligent agents. The agents work together for recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved
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.