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.
Due to the exponential growth of available text documents in digital form, it is of great importance to develop techniques for automatic document classification based on the textual contents. Earlier document classification techniques have used keyword-based features and related statistics to achieve good results when
measuring thedistance between categories and the assigned points, ranking of key wordswill be generated. Then, keywords are selected as attributes according to the rank, andtraining example for classifiers will be generated. Finally learning methodsare applied to the training examples. Experimental validation shows that random
, naive Bayes and rule-based (Ripper) classification algorithms for classification purpose. The classifiers from three algorithms were able to classify the tweets into one of six dialects with some error rate but the classifier study revealed that algorithms were able to pick the keywords that are the salient features of the
news sites discussing extremism and finally sites with no discussion of extremism. Then parts of speech tagging was used to find the most frequent keywords in these pages. Utilizing sentiment software in conjunction with classification software a decision tree that could effectively discern which class a particular page
small number of HTML input elements extracted from user input HTML forms and a few keywords. It utilizes pre-query technique and post-query technique in a hierarchical manner. Decision trees and multi layer artificial neural networks were used to obtain the classification rates over 91% to classify search forms and non
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.