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.
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the...
Network traffic classification plays an extremely important role in network management and service. Support vector machine (SVM) is widely adopted to classify traffic flows for its high accuracy. All features selected are treated equally in traditional SVM network traffic classification, which take little consideration of that each feature exerts a different influence on classification. Therefore,...
The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, the...
Recently a few works of semi-supervised learning methods based on graph have been proposed for remote sensing. The common idea of these methods are that they build a graph using the samples of the image. Most of their time complexity is relatively large, and they ignore the spatial information of the image, which leads to unsatisfactory classification results. this paper proposes a novel semi-supervised...
In this paper, an Arabic recommender system based on opinion analysis and polarity detection is proposed. Unfortunately, working with Arabic adds more difficulties than the other languages, because it implies the solving of different types of problems such as the diversity of dialects, Al hamza, the ambiguity, etc. These sorts of applications produce data with a large number of features, while the...
Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
Internet traffic classification has been researched extensively in the last 10 years, with a few different algorithms applied to it. Internet traffic classification has also become more relevant because of its potential applications in the business world. Having information about network traffic has many benefits in network design, security, management, and accounting. The classification of network...
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use...
Internet traffic classification has been studied widely in recent years, and many machine learning approaches have been applied to it. Internet traffic classification has increased in relevance in recent years because of its potential applications in the business world. Information about network traffic has many benefits in network design, security, management and accounting. Internet traffic classification...
Motor imagery (MI) based electroencephalogram (EEG) signals are a widely used form of input in brain computer interface systems (BCIs). Although there are a number of ways to classify data, a question still persists as to which technique should be employed in the domain of MI based EEG signals. In this paper, an attempt is made to find the best classification algorithm and feature extraction technique...
In many applications such as dealing with database, continuous environment and humanoid robots, the machine often deals with large amount of data every day of work. Dealing with large amount of data requires fast as well as accurate learning algorithms to do the classification. A new supervised non parametric Partial Histogram Bayes learning algorithm (PHBayes) is proposed and presented in this paper...
A novel image recognition method based on the improved BDBN (Bilinear Deep Belief Network) model is presented, optimized with a MKL (Multiple Kernel Learning) strategy. All kernel functions in MKL are replaced by hierarchical feature representations, and the number of kernels is set to the number of layers of BDBN. The method is performed on the standard Caltech101 image dataset. The experiments show...
Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising paths toward the understanding of fundamental questions in biology and medicine. High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Feature selection (FS) and clustering are among the methods used in...
Traffic classification is one of the kernel applications in network management. Many Machine Learning (ML) traffic classification algorithms are based on decision-trees. While most of the existing implementations of decision-trees are hardwarebased, a new trend in network applications is to use softwarebased solutions. The decision-tree used for traffic classification is highly unbalanced, it is challenging...
Imperfection of remote sensing data greatly affects the performance of information fusion algorithm. To solve this problem, a Gaussian kernel-based Fuzzy Rough Set fusion algorithm is proposed, since Fuzzy Rough Set theory is an effect tool to model uncertainties of data. For feature reduction a novel index is proposed to evaluate the significance of features, considering both the relevance between...
A great deal of effort is being made to increase accuracy and reliability of Condition Based Maintenance systems; for instance, by improved feature selection strategies or optimization approaches of classifier parameters. In this work a novel classification methodology is presented, covering from the characterization of the acquired physical magnitudes to the configuration of the classification algorithms...
Automatic seizure detection is very essential for monitoring and rehabilitation of epilepsy patients and will open up new treatment possibilities for saving the lives of epileptic patients. In recent years, many algorithms for the automatic seizure detection have been proposed and applied, in which Support vector machines proved to be a robust machine learning algorithm. The purpose of this study...
In biomedical area, information is mainly in natural language text format. Such information is stored in huge repositories. It is not easy to access required information from this large amount of data. Also the classification systems developed for general text is not applicable for biomedical data. The biomedical researchers need fast and accurate information accessing tools for extracting useful...
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.