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Fuzzy support vector machine (FSVM) have been very successful in pattern recognition problems with outliers or noises. FSVM enhances the SVM in reducing the effect of noises in data points. In this paper, we introduce FSVM to regression problems for function approximation with noises. We apply a fuzzy membership to each input point of SVR and reformulate SVR into fuzzy SVR (FSVR) such that different...
In this study, an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) learning approach is proposed for erratic demand forecast. This approach is under a "decomposition-and-ensemble" principal to decompose the original erratic demand series into several independent "smooth" subseries including a small number of intrinsic mode functions (IMFs) and a...
In this paper, an image retrieval framework combining content-based and content-free methods is proposed, which employs both short-term relevance feedback (STRF) and long-term relevance feedback (LTRF) as the means of user interaction. The STRF refers to iterative query-specific model learning during a retrieval session, and the LTRF is the estimation of a user history model from the past retrieval...
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