Traditional transfer learning classification algorithms solve related ( but not identical ) data classification issues by using a large number of labeled samples in the source domain and small amounts of labeled samples in the target domain. However, this technique does not apply to the transfer learning of data from different categories of learned source domain data. To solve this problem, we constructed a transfer learning constraint term using the source domain data and the limited labeled data in the target domain to generate a regularized constraint for the minimax probability machine. We propose a transfer learning classification algorithm based on the minimax probabil⁃ity machine known as TL⁃MPM. Experimental results on 20 Newsgroups data sets demonstrate that the proposed al⁃gorithm has higher classification accuracy for small amounts of target domain data. Therefore, we confirm the effec⁃tiveness of the proposed algorithm.%传统的迁移学习分类算法利用源域中大量有标签的数据和目标域中少量有标签的数据解决相关但不相同目标域的数据分类问题，但对于已知源域的不同类别数据均值的迁移学习分类问题并不适用。为了解决这个问题，利用源域的数据均值和目标域的少量标记数据构造迁移学习约束项，对最小最大概率机进行正则化约束，提出了基于最小最大概率机的迁移学习分类算法，简称TL⁃MPM。在20 News Groups数据集上的实验结果表明，目标域数据较少时，所提算法具有更高的分类正确率，从而说明了算法的有效性。
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