Researches On Algorithm And Application Of Ls-svm

Support Vector Machines(SVM) is a new developed machine learning method based on the Statistical Learning Theory(SLT) and optimization theory. It was proposed firstly by Vapnik and Cortes in 1995, it has become an important research direction in the field of machine learning. SVM has many advantages in pattern recognition, such as the superiority in small-sample and high-dimension problems, and it resolves the shortcomings of neural networks and other traditional classification methods effectively for its good performance and high generalization ability. Least Squares Support Vector Machines(LS-SVM) is developed from SVM. LS-SVM has all the advantages which SVM has, and it trains the model by linear equation group resolving, which reduces computing complexity and increases the solving speed, but not the quadratic programming problem as in SVM. However, as a new technique, researches on SVM still need be worked in theories and applications. The paper researches on SVM in model optimal selection and classification of special datasets in order to improve classification ability and generalization capacity of the classifier. The following parts are main works:1. Effects from LS-SVM model hyperparameter selection to classifiers were disscussed based on classification principle of SVM and LS-SVM. Then the facts were proposed that classifier with minimum structural risk is got just when penalty coefficient and kernel parameter are all appropriate.2. A method using estimation of distribution algorithms with diversity preservation (EDA-DP) to optimally select model parameters of LS-SVM was proposed, and the method was applied in recognition on UCI benchmark datasets and radar target high resolution range profile(HRRP) datasets. Experimental results showed that the classification model based on the algorithm had good ability.3. Under-sampling approaches for imbalanced data in feature space were discussed, and only when the distances between samples were smaller, some good effects of the classification on imbalance data would got by SVM which RBF was using.4. Cluster-based under-sampling and neighborhood cleaning approach(SBC-NCL) for imbalanced data classification by LS-SVM was proposed. The algorithm was applied in recognition on UCI benchmark datasets and radar target high resolution range profile(HRRP) datasets, and the results showed that the classification model based on the algorithm had good generalization capacity.