military conflict (Beck, King and Zeng 2000). In this study, seven dyadic variables are employed to predict MID outcome. 

Interstate conflict is a complex phenomenon that encompasses non-linear pattern of interactions (Beck, King and Zeng, 2000; Lagazio and Russett, 2003; Marwala and Lagazio, 2004). Various efforts have and still are underway to improve the MID data, the underlying theory and the statistical modelling techniques of interstate conflict (Beck, King and Zeng, 2000.) Previously, linear statistical methods were used for quantitative analysis of conflicts, which were far from satisfactory. The results obtained showed high variance, which make them difficult to be reliable (Beck, King and Zeng, 2000). The results have to be taken cautiously and their interpretations require prior knowledge of the problem domain. This makes it inevitable to look for techniques other than the traditional statistical methods to do quantitative analysis of international conflicts. Artificial intelligence techniques have proved to be very good in modelling complex and nonlinear problems without any *a priori* **constraints about the underlying** 
functions assumed to govern the distribution of MID data (Beck, King and Zeng, 2000). It then makes sense to model interstate conflicts using artificial intelligence techniques. 

Neural networks have previously been used by (Marwala and Lagazio, 2004; Beck, King and Zeng, 2000; Lagazio and Russett, 2003) to model MID. In this paper, two artificial intelligence techniques, neural networks and support vector machines, are used for the same purpose and their results were compared. These two techniques have been compared for other applications (e.g. for text texture verification (Chen and J. Odobez, 2002)] and for option pricing (Pires and 