t分布下基于核函数的最大后验概率分类方法

针对多元正态分布不能适应样本数据严重拖尾现象的问题，提出t分布下的多分类识别方法。利用核技术将样本数据扩展到高维特征空间中，采用贝叶斯分类器得到最大后验概率，进而得到分类结果。由于可以调整t分布中的自由度参数v,因此更容易满足数据样本的不同拖尾情况，具有较好的稳健性。在5个国际标准UCI数据集和3个人脸数据集上进行了大量实验，实验结果表明，该方法有较好的分类效果，具有可行性。

Maximum a posteriori classification method based on kernel method under t distribution

In order to solve the problem of multivariate normal distribution failing to comply with the distribution of sample data when it has a serious tailing phenomenon, a multiclass classification method under t distribution was proposed. Sample data were extended to the high dimensional feature space by kernel method and Maximum A Posteriori (MAP) was obtained by Bayesian classifier, and then classification result could be gotten. Because it has another degree of freedom parameter v in multivariate t distribution, it can more easily capture the complexity of the sample data, and enjoys better robustness. A large number of experiments have been done on the five international standard UCI data sets and three facial image data sets, and the experimental results show that the method achieves better classification and is feasible.