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相关向量机RVM

于 2023-04-23 发布 文件大小:31.56 kB
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它的训练是在贝叶斯框架下进行的,在先验参数的结构下基于主动相关决策理论(automatic relevance determination,简称ARD)来移除不相关的点,从而获得稀疏化的模型。在样本数据的迭代学习过程中,大部分参数的后验分布趋于零,与预测值无关,那些非零参数对应的点被称作相关向量(Relevance Vectors),体现了数据中最核心的特征。同支持向量机相比,相关向量机最大的优点就是极大地减少了核函数的计算量,并且也克服了所选核函数必须满足Mercer条件的缺点。

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