Titlebook: Applications of Soft Computing; Updating the State o Erel Avineri,Mario K?ppen,Rajkumar Roy Conference proceedings 2009 Springer-Verlag Ber

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https://doi.org/10.1007/978-3-8348-9159-4ay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two ot

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Advances in Intelligent and Soft Computinghttp://image.papertrans.cn/a/image/159566.jpg

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https://doi.org/10.1007/978-3-8348-9704-6d advantage of inherent dynamics, and are, therefore, well suited for handling temporal problems, such as process modeling and control. Our survey paper provides a general guideline for interested readers to choose, design, and apply these linguistic information feed-back- and feed-forward-based dynamical fuzzy systems in engineering.