Recently, a sparse kernel-based SVM ensemble learning technique has been introduced by the authors for hyperspectral plume detection/classification. This technique first randomly selects spectral feature subspaces from the input data. Each individual SVM classifier then independently conducts its own learning within its corresponding spectral feature space using a Gaussian kernel with a single bandwidth parameter. Each classifier constitutes a weak classifier. The sub-classifiers are sparsely weighted and aggregated to make an ensemble decision. In this paper, in order to further improve the generalization performance of the ensemble classifier, Gaussian kernel with full diagonal bandwidth parameter matrix is used for each sub-classifier where the parameters are optimally learned by minimizing a bound of the generalization error estimate using a gradient descent algorithm. A performance comparison between the aggregating techniques - sparse kernel-based technique and majority voting with single bandwidth and full diagonal optimized bandwidth parameters as applied to hyperspectral chemical plume detection is presented in the paper.