Abstract—Support Vector Machine (SVM) is a popular machine learning method for classification, regression, and other learning tasks. Support Vector Regression (SVR), a category for support vector machine attempts to minimize the generalization error bound so as to achieve generalized performance. Regression is that of finding a function which approximates mapping from an input domain to the real numbers on the basis of a training sample. Support vector regression is the natural extension of large margin kernel methods used for classification to regression analysis. In this paper Support Vector Regression is used to forecast the demand and supply of pulpwood. The usage of paper increases day to day. Wood Pulp is the most common raw material in paper making. On account of steady increase in paper demand, the forecast on demand and supply of pulp wood is considered to improve the socio economic development of India. Forecasting is done in Libsvm a library for support vector machines by integrating it with MATLAB.
Index Terms—Support vector machines (SVM), support vector regression (SVR), wood pulp, forecast, kernel.
V. Anandhi is with the Department of Forest Resource Management, Forest College and Research Institute, Mettupalayam 641 301, Tamil Nadu, India (e-mail: email@example.com).
R. Manicka Chezian is with the Department of Computer Science, NGM College, Pollachi- 642 001, Tamil Nadu, India.
Cite: V. Anandhi and R. Manicka Chezian, "Support Vector Regression to Forecast the Demand and Supply of Pulpwood," International Journal of Future Computer and Communication vol. 2, no. 3 pp. 266-269, 2013.