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.
With the growing use of RFID-based devices, there are increasing attentions on utilizing RFID technology for localization. In this paper, we consider RFID reader localization which locates an object by attaching it with an RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty in RFID reader localization is that frequent RFID faults can affect localization...
Accurate estimation of the canopy chlorophyll content of a crop is essential for crop production. Ground-based hyperspectral datasets were obtained under a wide range of plant and environmental conditions in Jilin using Analytical Spectral Devices(ASD) spectroradiometers, and canopy chlorophyll content in canopy were measured by Soil and Plant Analyzer Development(SPAD)-502. The objective of this...
The movement of price is influenced by many factors or features in stock market. It is a challenging work how to select these features and provide the relation between them and the movement of price. This paper applies two recursive feature elimination (RFE) methods SVM-RFE and RF-RFE to feature selection in the trend prediction of stock price, where SVM-RFE and RF-RFE are based on the famous support...
In this paper, we propose another extension of the Random Forests paradigm to unlabeled data, leading to localized unsupervised feature selection (FS). We show that the way internal estimates are used to measure variable importance in Random Forests are also applicable to FS in unsupervised learning. We first illustrate the clustering performance of the proposed method on various data sets based on...
We propose an alternative to univariate statistics for identifying population differences in functional connectivity. Our feature selection method is based on a procedure that searches across subsets of the data to isolate a set of robust, predictive functional connections. The metric, known as the Gini Importance, also summarizes multivariate patterns of interaction, which cannot be captured by univariate...
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.