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.
This paper provides a novel and unified framework of representation based classification technique. The proposed atomic representation based classification (ARC) framework includes, but not limited to, sparse representation based classification (SRC), low-rank representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant...
Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the device. Our recognition system is designed...
For classifier ensembles, an effective combination method is to combine the outputs of each classifier using a linearly weighted combination rule. There are multiple ways to linearly combine classifier outputs and it is beneficial to analyze them as a whole. We present a unifying framework for multiple linear combination types in this paper. This unification enables using the same learning algorithms...
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm is evaluated by its ability to generalize, i.e., to extend this mapping accurately to new data that is commonly referred to as the test data. Good generalization depends crucially on the quality of the training set. Because...
We study the problem of detecting and profiling terrorists using a combination of an ensemble classifier, namely random forest and relational information. Given a database for a set of individuals characterized by both "local" attributes such as age and criminal background, and "relational" information such as communications among a subset of the individuals, with a subset of the...
In this paper, we propose a max modular support vector machine (M2-SVM) and its two variations for pattern classification. The basic idea behind these methods is to decompose training samples of one class into several parts and learn each part by one modular classifier independently. To implement these methods, a dasiapart-against-otherspsila training strategy and a max modular combination principle...
Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as , achieves higher classification accuracy than traditional classification approaches such as C4.5. However, the approach also suffers from one major deficiency: a training data set often generates a huge set of rules. It is challenging to store,...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
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.