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.
Virtual learning community is a kind of learning environment based on network is a new type of learning organization. However, Virtual Learning Community in the teaching data is often messy, fragmentary, it's value is often difficult to be detected and reasonable to use data mining techniques to deal with data will give us a analysis to study the effect of get twice the result with half the effort...
Information technology has given e-retailers new capability of learning demand in real time. This paper investigates how to integrate this real time learning technology with Q-learning algorithm for the optimization of dynamic pricing in e-retailing setting. Especially, this paper studies the optimal dynamic pricing problem for seasonal and style products in e-retailing setting, and validate our approach...
Support vector machine (SVM) has been a promising method for data mining and machine learning in recent years. However, the training complexity of SVM is highly dependent on the size of a data set. A cluster support vector machines (C-SVM) method for large-scale data set classification is presented to accelerate the training speed. By calculating cluster mirror radius ratio and representative sample...
In this paper, we investigate the use of Q-learning approach to the problem of determining dynamic prices for multi-products in an e-retailing setting. In particularly, this article is concerned with the representation and generalization of large state spaces in Q-learning problems. We proposed a Q-learning model that is based on the self-organizing map, and validate our approach in simulated test.
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.