Spatial Choice Modeling Using the Support Vector Machine (SVM) :Characterization and Prediction

摘要

We take a cursory look at the support vector machine (SVM) as a useful and effective algorithm for characterizing and predicting spatial choice problems in economics. Beginning with a discussion of the SVM for the linearly separable case as well as the nonlinear non-separable case using the soft margin SVM and kernels, we then describe how the SVM can be used to characterize and predict spatial choice models, which can be seen as a special case of discrete choice models, using examples from a simple 1-D to the more complex multi-dimensional features space.