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Extreme learning machine (ELM), as an emergent technology, has attracted tremendous attention from various fields for its fast learning speed. Different from traditional gradient-based learning algorithms for feed-forward neural networks, ELM need not be neuron alike and learns with good generalization performance. However, ELM may require more hidden neurons than traditional tuning-based learning...
Recently, a novel family of unsupervised learning techniques, which is referred to as cluster ensemble, attracts great interest from computational intelligence communities. Cluster ensemble techniques combine multiple individual clustering solutions into a consensus one, and can provide more robust and frequently more accurate partitions when comparing to individual clustering methods. However, although...
Group consensus has both positive and negative communication weights, which is an extension to traditional consensus problem. Additionally, distributed event-triggered control has advantages over periodic control considering energy consumption and communication constraints. Thus, it is important to study group consensus using event-triggered control. Moreover, by calculating the maximum and minimum...
In this paper, a new predictive method based on Extreme Learning Machine is proposed to predict the spectrum data obtained from by frequency monitoring system of high-frequency radar. In order to improve the forecasting accuracy and real-time of spectrum prediction of high-frequency radar, Empirical Mode Decomposition method is used for the preprocessing of spectrum data. Based on the simulation environment...
Local detail features of face are important bases for recognizing different persons. For its invariant to monotonic gray-scale transformations and it's a non-parametric kernel which summarizes the local special structure of an image, the Local Binary Pattern (LBP) has becoming a popular technique for face representation. In this paper, the LBP technique and its application for representing faces are...
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