A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Reconfigurable pipelined 2-D convolvers for fast digital signal processing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Optimal control by least squares support vector machines
Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Efficient computations for large least square support vector machine classifiers
Pattern Recognition Letters
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Information enhancement for data mining
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Computers and Industrial Engineering
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Star acquisition and star pattern recognition are the most time-consuming routines in star tracker operation. To speed up the star acquisition procedure, the innovative efficient star cluster grouping method is based on the mapped least squares support vector machine (LS-SVM) with mixtures of radial basis function and polynomial kernels. By convolving star image with the second order directional derivative operators deduced from the mapped LS-SVM, the maximum extremum points (the possible center of stars) on the two-dimensional star image intensity surface are reliably determined, and then the star cluster grouping process in star acquisition procedure is significantly speeded up. The mixtures of kernels provide more optimal performance than any single kernel. Computer experiments for the simulated star images are carried out. The results demonstrate that the proposed algorithm is efficient and robust over a wide range of sensor noise.