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.
We report an experimental study that involves understanding how display (conventional or ecological) and system mode (profiting, neutral or losing) affect financial trading performance and risk preference. Twenty-four undergraduate and graduate student participants interacted with a financial trading simulator in the playback of a real market. Each participant completed a conventional display scenario...
Insulator identification in aerial videos is one of the key procedures to the condition analysis for aerial power line inspections. This paper proposes a novel insulator recognition method for images taken by Unmanned Aerial Vehicles (UAVs) with highly cluttered background, which is to adopt a machine learning algorithm Support Vector Machine (SVM) as a classifier to distinguish insulator from the...
Gestures contain enough information to distinguish a person from another. This ‘biometric’ feature is the basis of the Kinect-enabled system described in this paper, which is designed to identify a given person through a short chain of in-air gestures (2–3 ones). The user-dependent (trainable) system relies on a traditional Dynamic Time Warping classification algorithm. This core algorithm enhances...
Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained...
Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. Recent results show that maximizing the geometric mean or harmonic mean of Kullback-Leibler (KL) divergences of class pairs can significantly reduce this problem. In this...
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler...
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.