Showing papers on "Active learning (machine learning) published in 1980"
TL;DR: The nature of the aggregated learning curve is found to be different from its components (that is, the value of its parameters and its equation are not the same).
Abstract: The learning curve is one of the tools in operation planning and control. It enables the planner to estimate future performance of tasks executed either by individuals or organizations. In fact, every task is composed of elements, or subtasks. Therefore, the learning curve of the task is derived from the learning curves of the component elements and their frequency within the task. The paper discusses the nature of the aggregated learning curve as well as ways to calculate the values of its parameters. The nature of the aggregated learning curve is found to be different from its components (that is, the value of its parameters and its equation are not the same).
10 citations
TL;DR: This work proposes two routing methods, LRI and LRI, which are basically reinforcement methods for the learning automaton, and shows the effectiveness of these proposed methods by means of simulation on a concrete network topology.
Abstract: Learning routing is introduced to large-scale communication network to compensate the lack of a priori information on the network behavior. Learning routing consists of learning automaton as a learning controller placed at each network node. This learning automaton makes decision on routes for packets which are the basic unit of data to be transmitted through the network. Using learning automaton we propose two routing methods, LRI and LRI, which are basically reinforcement methods for the learning automaton. The effectiveness of these proposed methods is shown by means of simulation on a concrete network topology.