Foundation of Deep Machine Learning in Neural Networks

Abstract

This chapter introduces several basic neural network models, which are used as the foundation for the further development of deep machine learning in neural networks. The deep machine learning is a very different approach in terms of feature extraction compared with the traditional feature extraction methods. This conventional feature extraction method has been widely used in the pattern recognition approach. The deep machine learning in neural networks is to automatically “learn” the feature extractors, instead of using human knowledge to design and build feature extractors in the pattern recognition approach. We will describe some typical neural network models that have been successfully used in image and video analysis. One type of the neural networks introduced here is called supervised learning such as the feed-forward multi-layer neural networks, and the other type is called unsupervised learning such as the Kohonen model (also called self-organizing map (SOM)). Both types are widely used in visual recognition before the nurture of the deep machine learning in the convolutional neural networks (CNN). Specifically, the following models will be introduced: (1) the basic neuron model and perceptron, (2) the traditional feed-forward multi-layer neural networks using the backpropagation, (3) the Hopfield neural networks, (4) Boltzmann machines, (5) Restricted Boltzmann machines and Deep Belief Networks, (6) Self-organizing Maps, and (7) the Cognitron and Neocognitron. Both Cognitron and Neocognitron are deep neural networks that can perform the self-organizing without any supervision. These models are the foundation for discussing texture classification by using deep neural networks models.

Our greatest glory is not in never falling, but in rising every time we fall.

—Confucius