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Influence maximization problem aims at targeting a subset of entities in a network such that the influence cascade being maximized. It is proved to be a NP-hard problem, and many approximate solutions have been proposed. The state-ofart approach is known as CELF, who evaluates the marginal influence spread of each entity by Monte-Carlo simulation and picks the most influential entity in each round...
In this paper, we consider the problem of binary classification with imbalanced data. Although this problem has been studied extensively in terms of the classification performance, the probability estimation of both majority and minority class has not yet been well studied. In order to make precise class probability estimation (CPE), we propose a new approach of regression with a recently proposed...
With the introduction of trust mechanism into the wireless ad hoc network, security can be greatly improved, because trust information is shared among all nodes in the network. However, such a mechanism also has the potential vulnerability if the so-called 'big-mouth' nodes broadcast wrong trust information. Some measures have to be taken to filter the received trust information before it is fed to...
With highly correlated input signal, the variable step size (VSS) algorithms always suffer from a low convergence speed. One way to overcome this problem is to decorrelate the input signal before passing it to the equalizer. In this paper, we propose a novel variable step size least mean square (LMS) algorithm based on decorrelation. In this algorithm, the update of the coefficients is matched to...
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