Sparse Bayesian classification is applied to recognize and classify vehicle models.

利用稀疏贝叶斯分类器对检测到的汽车进行车型分类。

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We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.

我们将稀疏贝叶斯学习与序列蒙特卡罗盲均衡算法结合，提高了原算法的性能。

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Based on a rank-1 update, we propose sparse Bayesian Learning Algorithm (SBLA), which has low complexity and high sparseness, thus being very suitable for large-scale problems.

基于秩- 1更新，提出了稀疏贝叶斯学习算法(SBLA)。该算法具有较低的计算复杂度和较高的稀疏性，从而适合于求解大规模问题。

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