An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Symbolic Regression via Genetic Programming
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Robust polynomial neural networks in quantative-structure activity relationship studies
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
A (\mu + \lambda) - GP Algorithm and its use for Regression Problems
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Engineering Applications of Artificial Intelligence
Aggregate a posteriori linear regression adaptation
IEEE Transactions on Audio, Speech, and Language Processing
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A controlled genetic programming approach for the deceptive domain
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Global Positioning System (GPS) has been used extensively in various fields. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organized geometrically, so as a reliability indicator presenting the GPS positioning accuracy. Traditional methods for calculating GPS GDOP need to solve the measurement equations where involve complicated matrix transformation and inversion. Some studies rephrase the calculation of GPS GDOP a regression problem and employ ''black-boxed'' machine learning methods for problem solving. However, the regression models obtained from such methods lack of expressivity for describing the relationships among variables. Making the structures of GDOP expressions visible is valuable because they can be further studied or tailored for specific GPS applications. This study employs the technique of genetic programming (GP) for the regression of GPS GDOP. The performance of GP working with various operators and parameter settings is studied and discussed. The experimental results show that GP generates precise models with better expressivity for GPS GDOP than other methods.