It is based on individual, flexible models which are fitted on short segments of the available data and are learned simultaneously (in a global manner) using a coupling term that

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The classical support vector machines regression (SVMR) is known as a regularized learning algorithm in reproducing kernel Hilbert spaces (RKHS) with a ε -insensitive loss function

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Figure 1(b) demonstrates model prediction of proposed SLSSVR approach. It can be shown the linear MRLS algorithm has the worst performance in nonstationary case. Compared both the

Prediction functions that are defined using a sparse expansion are able to generalize far better since they consider only the most 'important' training points or

To illustrate some of the non-linear SVR solutions, various kernel functions were used to model the regression data in Table 5.1, with an -insensitive loss function ( = 0.5) and

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