Support vector machines for predicting the level of integration in agri-food chains

Abstract

The objective of this paper is derived from the theoretical analysis of the application of support vector machines to the design and management of agri-food chains. This analysis is conducted with an empirical approach, for the prediction of the level of integration in agri-food chains through support vector machines. The methodology designed and used for the processing of research results, which consists in the training of support vector machines is used to predict the level of integration in an agri-food chain. This type of predictive application appears in the literature consulted on the integration of agri-food chains. The analysis is performed comparing the method proposed with the neural network technique. The results of this research are mainly focused on predicting the level of integration in agri-food chains through vector machines. The study provides a support vector machine model that is applied to other case studies and therefore, allows predicting the outcome. The paper also shows the comparison of two techniques that share the goal of predicting, as applied in different contexts.