Prediction on the Collapse of Sewerage Systems by using Support Vector Machines

摘要

Support Vector Machine (SVM) is gaining much popularity as one of effective methods for machine learning in recent years. In pattern classification problems with two class sets, it generalizes linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. Linear classifiers then are optimized to give the maximal margin separation between the classes. In this paper, we apply support vector machine to the prediction on the collapse of sewerage systems which become superannuated, and investigate the effectiveness of using SVM.