基于分层高斯混合模型的半监督学习算法

提出了一种基于分层高斯混合模型的半监督学习算法，半监督学习算法的学习样本包括已标记类别样本和未标记类别学习样本。如用高斯混合模型拟合每个类别已标记学习样本的概率分布，进而用高斯数为类别数的分层高斯混合模型拟合全部(已标记和未标记)学习样本的分布，则形成为一个基于分层的高斯混合模型的半监督学习问题。基于EM算法，首先利用每个类别已标记样本学习高斯混合模型，然后以该模型参数和已标记样本的频率分布作为分层高斯混合模型参数的初值，给出了基于分层高斯混合模型的半监督学习算法，以银行票据印刷体数字识别做实验，实验结果表明，本算法能够获得较好的效果。

A Semi-Supervised Learning Algorithm Based on a Hierarchical GMM

A semi-supervised learning algorithm based on a hierarchical GMM is proposed. The learning samples in semi-supervised learning are a hybrid of labeled and unlabeled samples. If GMM is employed to fit the distribution of labeled samples in each class and a hierarchical GMM whose Gaussian number is the class number is employed to fit the distribution of the whole learning samples (including labeled and unlabeled samples), then a semi-supervised learning problem based on a hierarchical GMM has emerged. Based on EM algorithm, by learning the labeled samples of each class, a GMM is obtained first. Then by taking the parameters of the obtained GMM and frequencies of labeled samples as initials, a semi-supervised learning algorithm based on a hierarchical GMM is presented. Printed numerals in a bank check are tested in the experiments and the results shows the good effects of the proposed algorithm.