LS-SVMLeast Square Support Vector Machine
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Combined with the related research of SVM [18], the researchers put forward the LS-SVM, LS-SVM is an upgrade for the SVM, which changes the traditional SVM inequality constraints into equality constraints and taking the error squared and the loss function as the training set experience loss.
In order to improve the classification ability in real datasets, we adopt LS-SVM with kernel as the predictor.
Algorithms DGSMOTE OS-ELM MCOS-ELM LS-SVM Testing time (s) 0.
Additionally, when applying LS-SVM, it is recognizable that the tuning parameters, namely regularization and kernel function parameters, play an important role in establishing the predictive model (Yu et al.
Lastly, the PSO optimized LS-SVM is proposed to identify the plant operation states.
Theoretical backgrounds of RT, feature reduction and LS-SVM is described in Section 2, Section 3 and Section 4, respectively.
At the same time, the chemometrics methods of partial least-squares (PLS) and LS-SVM were compared, and the sensitive wavebands for N, P, and K were studied.
The LS-SVM was available as a software tool, developed at the Department of Electrical Engineering (ESAT), at the Katholieke Universiteit, Leuven.
Vapnik's SVM classifier formulation was modified by Suykens and Vandewalle (1999) into the following least-squares or LS-SVM formulation: