关键词: 监督学习, 霍夫森林, 半监督学习, 直推式支持向量机, 图像分类

Abstract: In order to improve the algorithm accuracy, supervised learning algorithms often require a lot of manual annotation of samples. Sample labeling process takes a lot of manpower and time. Therefore, how to quickly complete image annotation industry has been a hot research. This paper presents a semi-supervised learning algorithm based on Hough forest, with a relatively small sample of training the classifier, and continues to get new training samples in the classification process, improves the labeling efficiency. The result of algorithm experiments on the dataset of COREL shows that the algorithm can take advantage of a small amount of training samples, satisfactory labeling accuracy.

Key words: supervised learning, Hough forest, semi-supervised learning, Transductive Support Vector Machine（TSVM）, image classification