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.
Many real world classification problems lack of a large number of labeled data for learning an effective classifier. Active learning methods seek to address this problem by reducing the number of labeled instances needed to build an effective classifier. Most current active learning methods, however, are myopic, i.e. select one single unlabelled sample to label at a time. Obviously, such a strategy...
Hand gesture recognition aims to recognize the meaningful expressions of hand motion. It is widely used in information visualization, robotics, sign language understanding, medicine and healthcare. Some methods have been proposed for hand gesture recognition. But no single algorithm can handle all kinds of situations, because of the complex environment. In this study, we propose a hybrid method for...
According to the specific characteristics of samples, dynamic classifier ensemble chooses appropriate classifier for decision-making, which improve classification accuracy effectively, but increase the cost of running time. Therefore, Dynamic Combination of Multiple Classifiers Based on Central Similarity is proposed in this paper, which chooses different members classifier according to the similarity...
This paper improves CNM algorithm to detect community structure on weighted network. Based on the link weight and vertex weight, algorithm design defines a new Q-function to calculate community modularity, the type of communities were classified by finding the Q peak. We have generated networks with known community structure A,B and C(different sizes), to test if the algorithms can recognize and extract...
In this paper, Maximum Entropy (ME) framework is used to classify text documents. The ME framework has a lot of advantages when compared with other supervised learning algorithms, such as naive Bayes classifier. For example, it makes no inherent conditional independence assumptions between terms. With four labeled data sets, extensive experiments are made to compare the accuracy of ME algorithm with...
There is a vast amount of financial information on companies' financial performance. This information is of great interest for different stakeholders, i.e., stockholders, creditors, auditors, financial analysts, and managers. For stakeholders it is important to extract relevant performance information of the companies they are interested in. As a common method for classification and prediction, decision...
Naive Bayes Classifiers have been known with the advantages of high efficiency and good classification accuracy and they have been widely used in many domains. However, the classifiers need complete data. And the phenomenon of missing data widely exists in practice. Facing this instance, learning naive Bayes classifier and classification method with missing data are built in this paper. Compared with...
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.