An Improved Fast Sparse Least Squares Support Vector Machine (IFSLSSVM) is proposed for Synthetic Aperture Radar
(SAR) target recognition. Least Squares Support Vector Machine (LSSVM) is a least square version of Support Vector
Machine (SVM), but it lacks the sparseness compared with SVM. IFSLSSVM, which combines the incremental learning
and decremental learning, selects those important samples as the support vectors, and implements pruning by a certain
condition, can solve the non-sparse problem of LSSVM effectively. Benchmarking UCI datasets are firstly used for
testing the performance of our algorithm, followed by SAR target recognition. Experimental results on MSTAR SAR
dataset show that IFSLSSVM is an effective SAR target recognition approach (SAR-ATR), which not only reduces the
number of support vectors but also enhances the recognition rate.