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.
New strategies based on cognitive radio are being discussed to make a more efficient use of the HF band. Multiple users transmit in this band with a worldwide coverage but having multiple collisions with other HF stations. The use of the Upper Confidence Bound (UCB) algorithm is proposed in this work to provide them with a dynamic spectrum access mitigating mutual interference. Based on reinforcement...
In this paper, we present a heuristic-based algorithm to learn discriminative MLN structures automatically, directly from a training dataset. The algorithm heuristically transforms the relational dataset into boolean tables from which it builds candidate clauses for learning the final MLN. Comparisons to the state-of-the-art structure learning algorithms for MLNs in the three real-world domains show...
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing incremental learning in an adaptive...
In this paper, we present fast algorithms on mining coevolving time series, with or with out missing values. Our algorithms could mine meaningful patterns effectively and efficiently. With those patterns, our algorithms can do forecasting, compression, and segmentation. Furthermore, we apply our algorithm to solve practical problems including occlusions in motion capture, and generating natural human...
Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper, we have proposed the boosting method for face recognition (FR) that improves the linear discriminant analysis (LDA)-based technique. The improvement is achieved by incorporating the regularized LDA (R-LDA) technique into the boosting framework. R-LDA is based on a new regularized Fisher's discriminant...
Gait Energy Image (GEI) has been shown to be a robust gait descriptor for gait recognition, and many algorithms based on GEI have been proposed. We propose in this paper an improved algorithm to exploit the discriminative information of GEI in identifying walking people based on gait sequences. Specifically, we first obtain the discriminative power of each pixel in the GEI, referred to as feature...
This paper presents an original dynamic subsumption technique for Boolean CNF formulae. It exploits simple and sufficient conditions to detect, during conflict analysis, clauses from the formula that can be reduced by subsumption. During the learnt clause derivation, and at each step of the associated resolution process, checks for backward subsumption between the current resolvent and clauses from...
In recent years many approaches into Web-content development methods can be observed. In spite of many similarities in their description and formats; as in Dublin Core or LOM (learning object metadata), the characteristic features of these descriptors have not been strictly defined yet. What is more complexity of the available standards brings the user with difficulties to use them. The paper introduces...
Dynamic learning from RADAR data is a new challenge which needs the development of new classification methods using an online semi-supervised learning approach. Except a very recent paper presented at ECML 2008, no algorithm in the literature can deal with this problem. Based on a theoretical analysis of the limitations and advantages of several semi-supervised and online learning methods, we proposed...
A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are an effective way to characterize probabilistic and causal relations among variables providing a clear methodology for learning from observations. In recent years their use to recover transcriptional regulatory networks from...
The DIHMM algorithm performs a robust estimation of the HMM topology and parameters. It allows a better control of the speech variability within each state of the HMM, yielding enhanced estimates. The DIHMM parameters (number of states, structure of the Gaussian mixture density functions, transition matrix) are obtained from the training data via probabilistic grammatical inference techniques welded...
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.