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.
Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration is infeasible for either its prohibitive computational costs or access restrictions...
Classification is the one of the most important techniques in Datamining for data analysis. In Datamining, different Classification Techniques are available to predict outcome for a given dataset. There are many classification techniques for predicting and estimating accuracy, one such famous technique is Naïve Bayes Classifier. Naïve Bayes is very popular as it is easy to build, not so complex and...
It is necessary for a researcher to know historical transition in researchers and research topics. Although Web search engines can be used for obtaining such information, collecting the information across a long time period is difficult and laborious. Thus, we proposed a method for automatically extracting historical transition in researchers and research topics by using co-occurrence information...
Very Fast Decision Tree (VFDT) is an exemplar of classification techniques in data stream mining where models are built by incremental learning from continuously arriving data instead of batches. Many variations and modifications were made upon VFDT since it was first introduced in year 2000. Novel contributions were mainly made in two aspects of VFDT, tree induction process and prediction process,...
Quantification is the name given to a novel machine learning task which deals with correctly estimating the number of elements of one class in a set of examples. The output of a quantifier is a real value, since training instances are the same as a classification problem, a natural approach is to train a classifier and to derive a quantifier from it. Some previous works have shown that just classifying...
Consider multiple sensors that transmit data over analog erasure links to an estimation center. The sensors have access to distinct entries of the output vector of a linear and time-invariant plant, and the estimation center is intended to produce state estimates. If the estimation center can transmit noiseless acknowledgements back to the sensors at every time step, optimal algorithms to calculate...
Learning Bayesian networks can be examined as the combination of parameter learning and structure learning. Parameter learning is estimation of the conditional probabilities (dependencies) in the network. Structural learning is the estimation of the topology (links) of the network. The structure of the network can be known or unknown, and the variables can be expressed as complete and incomplete data...
Applying estimation of distribution algorithms (EDAs) to solve continuous problems is a significant and challenging task in the field of evolutionary computation. So far, various continuous EDAs have been developed based on different probability models. Initially, the EDAs based on a single Gaussian probability model are widely used but they have trouble in solving multimodal problems. Later EDAs...
Target intention inference is an important aspect of situation assessment. The evidence system of targets' intention inference is discussed according to the independent relationship between targets' intention and input evidence. The targets' intention probability inference model is proposed based on static Bayesian network. In order to expand the application domain and predigest the parameter learning...
Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification...
We consider problems where multiple agents cooperate to control their individual state so as to optimize a common objective while communicating with each other to exchange state information. Since communication costs can be significant, we seek conditions under which communication of state information among nodes can be minimized while still ensuring that the optimization process converges. In prior...
The paper discusses the use of decision trees for probability-based ranking. Emphasis is placed on ranking problems in question answering, where the frequency of correct candidates is very low but a single correct answer at one of the top ranks is often sufficient. Since existing tree learners handle this task poorly, decision tree induction is reformulated in such a way that it directly optimizes...
EDA-RL, estimation of distribution algorithms for reinforcement learning problems, have been proposed by us recently. The EDA-RL can improve policies by EDA scheme: First, select better episodes. Secondly, estimate probabilistic models, i.e., policies, and finally, interact with the environment for generating new episodes. In this paper, the EDA-RL is extended for multi-objective reinforcement learning...
Most monitoring or tracking applications require the localization information in wireless sensor network. The DV-Hop provides a basic scheme to retrieve the localization without GPS information. DV-Hop scheme only uses the localizations of the reference nodes and the hop-count, so the fundamental estimation may cause the larger error than range-based schemes. The proposed scheme uses the neighbor...
This paper describes a particle filter based approach for estimating the ground plane from an image sequence. Based on a Bayesian framework, the particle filter provides a robust estimation of the plane parameters, since it can handle non-linearities, while allowing a high flexibility for integrating new cues into the system. Furthermore, the different modes of the resulting probability density function...
In this study, we propose a gradual adaption model for a Web recommender system. This model is used to track users' focus of interests and its transition by analyzing their information access behaviors, and recommend appropriate information. A set of concept classes are extracted from Wikipedia. The pages accessed by users are classified by the concept classes, and grouped into three terms of short,...
Functional magnetic resonance imaging (fMRI) methods measure neuronal activity-induced changes indirectly by the blood oxygenation level dependent (BOLD) effect. The most comprehensive model to date of the BOLD signal is formulated as a mixed continuous discrete time system of nonlinear stochastic differential equations. Previous approaches have been based on linear approximations of the dynamics,...
The inherent properties of wireless sensor networks (WSN) disqualify most classic methods targeting timeliness guarantees. Assumptions of such methods as well as a restrictive notion of timeliness borrowed from classic real-time systems clash with the indeterminism of realistic scenarios. In this paper, we introduce a generalized notion of timeliness which allows to provide meaningful performance...
One of the most important aspects of a WSN is its localization scheme. Adequate accuracy, complexity, timing, and vulnerability to environment pitfalls are all worth considering parameters. Here, we propose discrete probabilistic DV-Hop, an enhancement to DV-Hop, a well-known range-free WSN localization algorithm, aiming to improve both its localization accuracy and complexity. One should note that...
Distance measurement between nodes in wireless sensor networks is a prerequisite for a variety of applications and algorithms. However, special hardware allowing such measurements is expensive, especially if dealing with hundreds or thousands of nodes. Fekete et al. presented an approach on distance estimation based on only the neighborhood information available to all nodes in the network. We improve...
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.