混合深度学习模型C-RF及其在手写数字识别中的应用
Hybrid Deep Learning Model C-RF and Its Application in Handwritten Numeral Recognition

卷积神经网络（Convolutional neural network，CNN）是一种常见的深度学习模型，受人类视觉认知机制启发而来，能够从原始图像得到有效的特征表达。CNN模型在图像识别领域不断取得突破，但是在训练过程中需要花费大量时间。随机森林（Random forest，RF）在分类和回归上具有很高的精度，训练速度快并且不容易出现过拟合的问题，现有的基于RF的分类器都依赖手工选取的特征。针对以上问题，本文提出了基于CNN的C-RF模型，把CNN提取到的特征输入RF中进行分类。由于随机权值网络同样可以得到有效的结果，所以不用梯度算法调整网络参数，以免消耗大量时间。最后在MNIST数据集和Rotated MNIST数据集上进行了实验，结果表明C-RF模型的分类精度比RF有了较大的提高，同时泛化能力也有所提升。
Convolutional neural network (CNN) is a kind of common architecture of deep learning, which is inspired by the biological visual cognition mechanism. CNN can obtain the effective feature expression from the original image. In recent years, CNN has made breakthroughs in the field of image recognition, but it takes a lot of time in the training process. As a new machine learning algorithm proposed by Leo Breiman in 2001, random forest (RF) has high accuracy in classification and regression, fast training speed and is not prone to over-fitting. The existing RF based classifiers rely on hand-selected features. Aiming at the above problems, a new C-RF model based on CNN is proposed in this paper, which puts the features extracted by CNN into RF to complete the classification.Since the network using random weights can also obtain effective results, gradient algorithm is not used to adjust the network parameters for avoiding a lot of time consumption. Experimental results on the MNIST and the Rotated MNIST datasets show that the classification accuracy of C-RF model is better than that of RF, and the generalization ability is also improved at the same time.