计算机科学 ›› 2016, Vol. 43 ›› Issue (Z6): 46-50.doi: 10.11896/j.issn.1002-137X.2016.6A.010
朱常宝,程勇,高强
ZHU Chang-bao, CHENG Yong and GAO Qiang
摘要： 近年来,深度学习在图像、语音、视频等非结构化数据中获得了成功的应用,已成为机器学习和数据挖掘领域的研究热点。作为一种监督学习模型,成功的深度学习应用往往要求较大的高质量的训练集。基于此,研究了多个受限波尔兹曼机组成的深度信念网络,结合半监督学习的思想,使用较小的训练集提高深度网络模型的分类准确性。分别采用了Knn,SVM和pHash 3种方法来学习非标示数据集,实验结果表明半监督深度信念网络比传统多层受限波尔兹曼机在图像分类准确率方面提高了约3%。
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