Representing Temporal Knowledge for Case-Based Prediction
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
An Automated Hybrid CBR System for Forecasting
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Probabilistic Indexing for Case-Based Prediction
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
An adaptive nearest neighbor search for a parts acquisition ePortal
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Model-guided information discovery for intelligence analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Case base maintenance for improving prediction quality
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
The virtue of reward: performance, reinforcement and discovery in case-based reasoning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Case adaptation with qualitative algebras
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features such as the (x,y) coordinates of an event. The case-based prediction is particularly well suited to deal with non-geographical features such as the time of the event or income level of the population. We claim that the combination of these two predictors results in a significant improvement of the accuracy in the spatial event prediction compared to pure geographically-based predictions. We support our claim by reporting on an ablation study for the prediction of improvised explosive device (IED) attacks.