The results found show that SVM has better capacity in forecasting conflicts without effectively affecting the correct peace prediction than NN. Two separate experiments were conducted to see the influence of each variable to the MID outcome. The first one assigns each variable to its possible highest value while keeping the rest to their possible lowest values. The NN results show that both democracy *level* and *capability ratio* **are able to influence the outcome to be peace. On the** 
other hand, none of the variables was able to influence the MID outcome to be conflict when all the other variables were maximum. SVM was not able to pick the effects of the variable for this experiment. 

The second experiment assigns each variable to its possible highest or lowest value while keeping the other variables fixed to their original values. The results agree with the previous experiment. If we group the variables in terms of their effect and rank them, Democracy level and capability ratio are first, contiguity, distance and alliance second and dependency, major power are ranked third using NN. Although SVM performs better than NN, the results of NN are easier to be interpreted in relation to variable influence. 

## References

Beck, N., King, G.and Zeng, L. (2000), Improving Quantitative Studies of International Conflict: A Conjecture. American Political Science Review, vol. 94, no. 1, pp. 21–33, March 2000. Bishop, C. (1995), Neural Networks for Pattern Recognition. Oxford, UK: Oxford University Press Burges, C. (1998), A Tutorial on Support Vector Machines for Pattern Recognition., Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, Chen, D and J. Odobez. (2002), Comparison of Support Vector Machine and Neural Network for Text Texture Verification, IDIAP-RR-02 19, IDIAP, Martigny, April 2002. Ftp://ftp.idiap.ch/pub/reports/2002/rr-02-19.pdf. 