Marwala, 2004), etc. Their findings show that SVMs have outperformed NNs. These results motivate the use of SVMs to model interstate conflict. 

## Ii. Background A. Learning Machines

There are different types of learning machines used for different kinds of purposes. The two major applications of learning machines are for classification and regression. Learning problem for classification can be defined as finding a rule that assigns objects into different classes (Müller et al, 2001). The rule that governs the classification is devised based on an acquired knowledge about the object. The process of knowledge acquisition is called training. In this paper, we look at two different types of learning machines for the purpose of predicting MID. Both techniques learn an underlying pattern based on training MID data to predict previously unseen (test) MID data. B. Artificial Neural Networks (NNs) 
Neural network is a processor that resembles the brain in its ability to acquire knowledge from its environment and store the information in some synaptic weights (Haykin, 1999). It is composed of simple neurons that are capable of processing information in a massively parallel fashion and can also make generalisations once they are trained using training data. This property gives neural networks the ability to solve complex non-linear problems (Haykin, 1999). 

The most widely used neural network is a feed-forward network with two layers of adaptive weights (Bishop, 1995). As it is shown in figure 1, it has input, hidden and output layers. The 