Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

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A New Method for Predicting the Ingredients of Self-Compacting Concrete (SCC) Including Fly Ash (FA) Using Data Envelopment Analysis (DEA) (2020, background): “The knowledge that is obtained during the training of the model is implicitly stored, and therefore, a logical commentary of the overall structure of the network is very hard to come up [3, 29].”
Prediction of rainfall time series using modular soft computingmethods (2013, background): “The nonlinear SVR problem can be expressed as the 9 following optimization problem (Kecman, 2001; Yu et al., 2006): 10 * 2 * , , i =1 i i * i * 1 ( ) 2 y ( ( ), ) ( ( ), ) y , 0 i i n i i i i i i i minimize R C f b subject to f b”
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, methodology): “Conversely, the CDSVR model preferred fewer training data, which exponentially improves its training speed but does not obviously affect its generalization due to the implementation of structural risk minimization (SRM) in SVR [Kecman, 2001].”