A FAST DEEP STACKED NETWORK USING EXTREME LEARNING MACHINE
Access to large amounts of data is becoming more common, as well as the use of methods based on "deep" learning to obtain better results. However, using those techniques can lead to long training times. To deal with this problem, the Deep Stacked Network (DSN) was proposed, where several small modules are stacked to increase the model eciency. However, this architecture suers from some problems that increase its training time and the amount of memory required to store it. To deal with some of these problems, we propose in this paper a fast algorithm to train a DSN using Extreme Learning Machine (ELM). Experiments performed on many classication datasets showed that the proposed method achieves similar accuracy when compared with other techniques, with the advantage of training the network in less time and storing fewer parameters.