稀疏贝叶斯模型与相关向量机学习研究

虽然支持向量机在模式识别的相关领域得到了广泛应用,但它自身固有许多不足之处.相关向量机是在稀疏贝叶斯框架下提出的稀疏模型,模型没有规则化系数,核函数不要求满足Mercer条件.相关向量机不仅具备良好的泛化能力,而且还能够得到具有统计意义的预测结果.首先介绍了稀疏贝叶斯回归和分类模型,通过参数推断过程,将相关向量机学习转化为最大化边缘似然函数估计,并分析了3种估计方法,给出了快速序列稀疏贝叶斯学习算法流程.

Research on Sparse Bayesian Model and the Relevance Vector Machine

The support vector machine is successfully applied in many fields of pattern recognition, but there ere several limitations thereof. The relevance vector machine is a I3ayesian treatment, its mathematics model doesn't have regularination coefficient, and its kernel functions don' t need to satisfy Mercer' s condition. The relevance vector machine can present the good generalization performance, and its predictions are probabilistic. We introduced the sparse I3ayesian modes for regression and classification, regarded the relevance vector machine learning as the maximization of marginal likelihood through the model parameters inference, then we described three kinds of training methods and presented the flow of the fast sequential sparse I3ayesian learning algorithm.