cid 0b0f1de19eabf7fea7362a09c39f4d0586f902264388db8824c68147a16d659d · original · open access · via arxiv
Title: A Tutorial about Random Neural Networks in Supervised Learning Authors: Sebastián Basterrech; Gerardo Rubino Venue: arXiv Year: 2016 Canonical: arxiv:1609.04846v1 URL: http://arxiv.org/abs/1609.04846v1 Collector: arxiv OA: true ABSTRACT Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and also as models of neurological aspects of living beings. In this article we focus on their learning capabilities, and more specifically, we present a practical guide for using the RNN to solve supervised learning problems. We give a general description of these models using almost indistinctly the terminology of Queuing Theory and the neural one. We present the standard learning procedures used by RNNs, adapted from similar well-established improvements in the standard NN field. We describe in particular a set of learning algorithms covering techniques based on the use of first order and, then, of second order derivatives. We also discuss some issues related to these objects and present new perspectives about their use in supervised learning problems. The tutorial describes their most relevant applications, and also provides a large bibliography.