The SVM-Based Prediction of Periodical Circulation and Procurement Costs in a University Library

Abstract

In order to effectively use books and reasonably distribute purchasing funds, the Support Vector Machines (SVM) method is used in this paper to establish a mathematics model for the related historical data of the library at Beijing University of Civil Engineering and Architecture. The book circulation and the allocation proportion of purchasing funds in the future are predicted based on the model. It is shown that the SVM method is feasible in predicting the book circulation and allocation proportion of purchasing funds with high non-linearity even if the size of a sample is small.