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中文摘要：
该文提出一种基于二级先验概率的多元核Logistic分类机,扩展核Logistic回归为多元模型,并解决其解的稀疏性问题,以提升多分类应用时的模型运行速率。为约简模型构建所需计算量,训练过程采用自下向上增补算法,每次迭代采用尽量少的输入样本,规避了大型矩阵逆操作,以适应于不同量度的数据场合。实验显示,所提多元分类机模型构建简单,且识别率与稀疏性都优于经典支持向量机所生成的＂一对一＂多分类方法及传统多元核Logistic回归算法。
英文摘要：
A new kernel logistic regression model based on two phase sparsity-promoting prior is proposed to render a sparse multi-classifier and enhance the run-time efficiency.For accelerating the building of the model,the bottom-up training algorithm is adopted which controls the capacity of the learned classifier by minimizing the number of basis functions used,resulting in better generalization and faster computation.Experimental results on standard benchmark data sets attest to the accuracy,sparsity,and efficiency of the proposed methods.
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