The nature of statistical learning theory
The nature of statistical learning theory
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Chattering-Free LS-SVM Sliding Mode Control
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
DIGMAP-detector: an intelligent computerized tool to detect and predict digital map pattern
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Introducing an intelligent computerized tool to detect and predict urban growth pattern
WSEAS Transactions on Computers
A multi-agent system for web-based risk management in small and medium business
Expert Systems with Applications: An International Journal
Cellular automata based on artificial neural network for simulating land use changes
Proceedings of the 45th Annual Simulation Symposium
Support vector machine for multi-classification of mineral prospectivity areas
Computers & Geosciences
Cellular automata model based on machine learning methods for simulating land use change
Proceedings of the Winter Simulation Conference
Modelling land-use effects of future urbanization using cellular automata: An Eastern Danish case
Environmental Modelling & Software
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Cellular automata (CA) have been increasingly used to simulate urban sprawl and land use dynamics. A major issue in CA is defining appropriate transition rules based on training data. Linear boundaries have been widely used to define the rules. However, urban land use dynamics and many other geographical phenomena are highly complex and require nonlinear boundaries for the rules. In this study, we tested the support vector machines (SVM) as a method for constructing nonlinear transition rules for CA. SVM is good at dealing with nonlinear complex relationships. Its basic idea is to project input vectors to a higher dimensional Hilbert feature space, in which an optimal classifying hyperplane can be constructed through structural risk minimization and margin maximization. The optimal hyperplane is unique and its optimality is global. The proposed SVM-CA model was implemented using Visual Basic, ArcObjects^(R), and OSU-SVM. A case study simulating the urban development in the Shenzhen City, China demonstrates that the proposed model can achieve high accuracy and overcome some limitations of existing CA models in simulating complex urban systems.