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.
Metric learning, the task of learning a good distance metric, is a key problem in machine learning with ample applications. This paper introduces a novel framework for nonlinear metric learning, called kernel density metric learning (KDML), which is easy to use and provides nonlinear, probability-based distance measures. KDML constructs a direct nonlinear mapping from the original input space into...
Investigating the pattern of host load in computing systems is very useful for discovering the data features and predicting the host load in the future. Since the host load can be regarded as the time series data, this paper proposes a pattern discovery framework for host load data by applying time series analysis methods. In the proposed framework, the effective data representation, data segmentation...
This paper presents a new method for efficient and exact collision-checking of linear motions of 3-D rigid bodies. 3-D rigid bodies have 6-D configuration spaces (three degrees of freedom for position and three for orientation), and constitute an important subclass of motion planning problems. Our method can be used with any collision-checker that is capable of performing linear transformations and...
The current Web manifests the problem of information overload due to the success of the Web 2.0 paradigm in which users can provide new contents quickly. To help users find the most valuable information, a recommendation system is designed in which we use Euclidean formula to calculate the distance and Cosine formula to calculate the angle to distinguish between different kinds of users. Thus, similar...
This paper introduces a supervised metric learning algorithm, called kernel density metric learning (KDML), which is easy to use and provides nonlinear, probability-based distance measures. KDML constructs a direct nonlinear mapping from the original input space into a feature space based on kernel density estimation. The nonlinear mapping in KDML embodies established distance measures between probability...
Indoor positioning in wireless local area network (WLAN) has been attracting increasing attentions for its cost effectiveness and reasonable positioning accuracy. Existing positioning methods all pay attention to establish more accurate relationship between received signal strength (RSS) and physical locations. However, the deployment of access points (APs) is ignored. This paper proposes an optimized...
This paper proposed a novel method, which uses gray level co-occurrence matrix (GLCM) and singular value decomposition (SVD) to extract the features of texture images for image retrieval. In our algorithm, we take the top ten singular values which are based on SVD as the feature vectors, and then uses the Euclidean distance for similarity measure. Compared with traditional GLCM-based feature extraction...
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.