Learning Algorithm of Dynamic Least Square Support Vector Machine

Aiming at solving the training problem of large-scale sample sets and model modifying problem of dynamic training sets,the dynamic least square support vector machine method is presented.The novel method can take full advantage of the model which is built by incremental algorithm.Based on the updated model,the new samples can be added gradually,the non-support vectors located in any position of the training set can be found and deleted easily.The matrix inverse algorithm is avoided;then a high calculation efficiency can be obtained theoretically.Two examples,one is dam deformation prediction and the other is ionosphere delay prediction,show the excellent performance of the proposed algorithm in modeling time and prediction precision.All the samples indicate that the method proposed is superior than other methods at present.