Modeling　land-use　change　is　a　prerequisite　to　understanding　the　complexity　of　land-use-change　patterns.　This　paper　presents　a　novel　method　to　model　urban　land-use　change　using　support-vector　machines　(SVMs),　a　new　generation　of　machine　learning　algorithms　used　in　classification　and　regression　domains.　An　SVM　modeling　framework　has　been　developed　to　analyze　land-use　change　in　relation　to　various　factors　such　as　population,　distance　to　roads　and　facilities,　and　surrounding　land　use.　As　land-use　data　are　generally　unbalanced,　in　the　sense　that　the　unchanged　data　overwhelm　the　changed　data,　traditional　methods　are　incapable　of　classifying　relatively　minor　land-use　changes　with　high　accuracy.　To　circumvent　this　problem,　an　unbalanced　SVM　has　been　adopted　by　enhancing　the　standard　SVMs.　A　case　study　of　Calgary　land-use　change　demonstrates　that　the　unbalanced　SVMs　can　achieve　high　and　reliable　performance　for　land-use-change　modeling.