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Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability,(More)
Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised(More)
How to effectively protect against spam on search ranking results is an important issue for contemporary web search engines. This paper addresses the problem of combating one major type of web spam: 'link spam.' Most of the previous work on anti link spam managed to make use of one snapshot of web data to detect spam, and thus it did not take advantage of(More)
In this paper, a class of recurrent neural networks with discrete and continuously distributed delays is considered. Sufficient conditions for the existence, uniqueness, and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations. The proposed(More)
We studied the Cauchy problem of fuzzy di erential equations on the basis of introducing the concept of di erentiation which is a generalization of the de nition of H-di erentiability due to Puri and Ralescu (1983), for fuzzy set-valued mappings of real variables whose values are in the fuzzy number space (E; D). The existence and uniqueness theorem is(More)
In this paper, a robust support vector regression (RSVR) method with uncertain input and output data is studied. First, the data uncertainties are investigated under a stochastic framework and two linear robust formulations are derived. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective. Second,(More)
To reduce the communication cost of a sensor node, this paper is concerned with an estimation framework with scheduled measurements for a linear system. A scheduler is designed to control the transmission of measurements from sensor to estimator, which results in that only a subset of measurements is transmitted to the estimator. We propose an innovation(More)
In this paper a sufficient condition is presented to ensure the complete stability of delayed CNNs. Such a condition establishes a relation between the time delay and the parameters of the networks. Specially, for a given output function f(x) = tanh(x), we address a sufficient condition to ensure absolute convergence of system state.