Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks
This work addresses the need for reduced training time in deep learning for real-time decision systems, but it appears incremental as it builds on existing Deep ELM methods without introducing new paradigms.
The study compared Deep Extreme Learning Machines (Deep ELM) to Convolutional Neural Networks (CNNs), highlighting Deep ELM's faster training times and effectiveness for real-time classification problems, though specific numerical gains were not provided.
Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the graphical processing unit capabilities. Increasing number of the neuron sizes at each layer and hidden layers is directly related to the computation time and training speed of the classifier models. The classification parameters including neuron weights, output weights, and biases need to be optimized for obtaining an optimum model. Most of the popular DL algorithms require long training times for optimization of the parameters with feature learning progresses and back-propagated training procedures. Reducing the training time and providing a real-time decision system are the basic focus points of the novel approaches. Deep Extreme Learning machines (Deep ELM) classifier model is one of the fastest and effective way to meet fast classification problems. In this study, Deep ELM model, its superiorities and weaknesses are discussed, the problems that are more suitable for the classifiers against Convolutional neural network based DL algorithms.