The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
One class SVM for yeast regulation prediction
ACM SIGKDD Explorations Newsletter
An Equivalence Between Sparse Approximation and Support Vector Machines
An Equivalence Between Sparse Approximation and Support Vector Machines
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Data Mining: Next Generation Challenges and Future Directions
Data Mining: Next Generation Challenges and Future Directions
Estimating the Support of a High-Dimensional Distribution
Neural Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A statistical threat assessment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Crime hot-spot location prediction is important for public safety. The output from the prediction can provide useful information to improve the activities aimed at detecting and preventing safety and security problems. Location prediction is a special case of spatial data mining classification. For instance, in the public safety domain, it may be interesting to predict location(s) of crime hot spots. In this study, we present a support vector machine (SVM)-based approach to predict the location as an alternative to existing modeling approaches. Support vector machine forms the new generation of machine-learning techniques used to find optimal separability between classes within datasets. We compare the performance of two types of SVMs techniques: two-class SVMs and one-class SVMs. We also compared SVM with a neural network-based approach and spatial auto-regression-based approach. Experiments on two different spatial datasets demonstrate that the former approach performs slightly better and the latter one gives reasonable results. Furthermore, in this study, we provide a general framework to customize the spatial data classification task for other spatial domains that have datasets similar to the analyzed crime datasets.