Research On Image Classification Based On Deep Learning Of Extreme Speed Learning Machine

With the development of the information technology, Deep Learning based on neural networks has become the synonym of all kinds of advanced technology. Since the neural network technology appeared in the last century, so many models based on neural networks have been used to solve different problems in real world. Especially in 2012, the models based on deep learning raised the accuracy by 11 percent, in the Image Net Large Scale Visual Recognition Challenge(ILSVRC), which led to a new wave of deep learning. Deep learning can learn effective feature automatically instead of obtaining feature artificially, which can reinforce the ability of abstract learning, where the deep learning improve the development of image detection, image recognition, speech recognition, tracking and so on.Extreme learning machine was proposed in 2004, and in past decades, has made great strides forward. First of all, many theories have proposed to support the development of the extreme learning machine; Secondly, extreme learning machine has been extended to real world to solve real problems. But in extreme learning machine, no matter in weights initialization, feature representation and in deep learning, there are many optimized problems need to be solved.This paper has explored the extreme learning machine and tried to solve three problems:First of all, this paper has explored the weight initialization of extreme learning machine and tried to illustrate the effect when different methods are used. The proposed method solves the problem that the random generated feature is lack of compactness and discriminant, and provides a fast and effective method to initialize a neural network model.Secondly, this paper has explored the feature representation of extreme learning. Distributed representation is a common method in feature representation. Without tuning the weights of extreme learning machine iteratively, this paper has proposed the distributed representation based on extreme learning machine. It not only adds the category information into the model by combining the features, but also improves the performance of extreme learning machine.Thirdly, this paper has explored the relationship between fully connected neural networks and convolutional neural networks and proposed the convolutional extreme learning machine based on distributed representation, and extend extreme learning machine to the deep learning field in the aspect of convolution. Convolutional learning can learn more local information using the local receptive fields, which can learn a more represented feature. Extreme learning machine can obtain more abstract and represented convolutional feature by using convolutional operation. It is not only a fusion of extreme learning machine and convolutional neural networks, but also achieves an excellent performance.