Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems

@article{Gurram2013SparseKE,
  title={Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems},
  author={Prudhvi Gurram and Heesung Kwon},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2013},
  volume={51},
  pages={787-802}
}
Recently, a kernel-based ensemble learning technique for hyperspectral detection/classification problems has been introduced by the authors, to provide robust classification over hyperspectral data with relatively high level of noise and background clutter. The kernel-based ensemble technique first randomly selects spectral feature subspaces from the input data. Each individual classifier, which is in fact a support vector machine (SVM), then independently conducts its own learning within its… CONTINUE READING

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  • On an average, the proposed sparse kernel-based ensemble learning algorithm with optimized full-diagonal bandwidth parameters shows an improvement of 20% over the existing ensemble learning techniques.

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