Vehicle Recognition And Earthquake Prediction By Using Support Vector Machine

Statistical Learning Theory (SLT) proposed by Vapnik et al is a statistics theory for the analysis of a small-sample database. Based on SLT, Support Vector Machine (SVM) was proposed as a new machine learning method for classification or regression. Based on Structural Risk Minimization Rule (SMR), SVM usually achieves better generalization performance than the Empirical Risk Minimization (ERM)-based learning methods, such as Bayesian Classifier (BC), Decision Tree (DT), and Artificial Neural Network (ANN). Up to now, SVM has been widely and successfully employed in various fields.In this study, the features of vehicle profile, acoustic and seismic signals were used to recognize the types of vehicles by using SVM approach. The effect of different feature extraction and selection methods on the classification accuracy was analyzed and discussed. We also compared the generalization performance of SVM with those of other classifiers.For the first time, we proposed and applied SVM to predict earthquake.The outline of this paper is showed as below:(1) The current methods of feature selection and extraction were reviewed. The advantages and disadvantages of several algorithms were introduced, such as, Genetic Algorithm (GA), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Particle Swarm Optimization (PSO), simulation annealing (SA).(2) The classification principles of popular classifiers were reviewed briefly, such as BC, DT, and ANN. We described SVM detailedly in its principle, algorithm, implementation, development and application.(3) We employed SVM and the features of vehicle profile, acoustic and seismic signals to recognize the types of vehicles, and analyzed the effect of different feature selection and extraction methods on the classification accuracy. We also compared the classification performance of different classifiers for vehicle recognition. The experimental results demonstrate that the accuracy of SVM is superior to those of other classifiers, and also reveal that PCA is more effective and faster than GA for implemention of feature dimension reduction. Under using a same classifier, the accuracies for either the training or test dataset by using PCA are higher than those of by using GA. It was found that the higher accuracy can be obtained via using the...