linear kernel function. Moreover, the polynomial kernel has a number of hyperparameters which influences the complexity of the model and some times its values may become infinity or zero as the degree becomes large. This makes RBF to be a common choice for use. 

Similar to the neural network, SVM also requires selection of a model that gives an optimal result. The conducted experiments show that RBF gives best results for the classification of MID data in much less time. When RBF is used as the kernel function, there are two parameters that are required to be adjusted to give the best result. These are the penalty parameter of the error term C and the γ **parameter of the RBF kernel function. Cross-validation and grid-search are two methods** 
which are used to find an optimal model. For this experiment, a 10-fold cross-validation technique and a simple grid-search for the variables C and γ were used. The parameters C = 1 and γ **= 16.75** 
gave the best results. 

## C. Mid Data

The data sets for the experiment, as discussed in the previous section, came from the Correlates of War (COW) that was compiled and used by Russett and Oneal (Russett and Oneal, 2001). It includes politically relevant dyads for the cold war and immediate post-cold war period (CW), from 1946 to 1992. As it is described in (Marwala and Lagazio, 2004; Lagazio and Russett, 2003; Oneal and Russett, 2001; Oneal and Russett, 1999), politically relevant dyads refer to all those which are contiguous and contain major power. Distant and weak dyads are omitted from the data set because it is less probable for them to have conflicts. Since the aim is to predict the onset of a conflict rather than its continuation, the dyads include only those with no disputes or only the initial year of the 