Improvement of Automated Learning Methods based on Linear Learning Algorithms
Journal Title: International Journal of Machine Learning and Networked Collaborative Engineering - Year 2018, Vol 2, Issue 2
Abstract
In recent years, the learning methods are converted to one of the new research area. These researches are divided into two general categories. The first category recognizes the principles of learning the living entities and its stages. The second is learning based methodology to any machines that the proposed method of this paper is based on it. Learning is defined as changes made in the performance of a system based on experiences. An important feature of learning systems is the ability to improve their efficiency over time. In mathematical terms, it can be stated that the purpose of a learning system is to optimize a task that is not well-known. Therefore, an approach to this problem is to reduce the goals of the learning system to an optimization problem. So, it is defined on a set of parameters and its purpose is to find the optimal set of parameters. In many of the issues raised, there is no knowledge of the correct answers to the problem in supervised learning based methods especially. For this reason, the use of a learning method called reinforcement learning has been considered. The main advantage of this technique over other learning methods is the need for no information from the environment (except amplification signal). The other learning methods as supervised or unsupervised are not appropriate to these problems. In this method, each agent decides the next its actions based on current k-actions instead of one action. In this paper is proposed a new approach based on the reinforcement learning technique that has three versions in order to implementation in different areas. It behaviors based on reward and penalty model. The effectiveness of these interactions with the environment is evaluated by the maximum and minimum of the number of rewards and penalties that are taken from the environment. The three versions are simple, sequential and unstructured linear learning methods so they evaluated in different possibilities to get the appropriate responses. Depending on the needs of any system, they can be used. The mode of convergence of actions in the proposed automaton (machine) in six different scenarios is examined.
Authors and Affiliations
Farzad Kaini
Keywords
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- EP ID EP371014
- DOI 10.30991/IJMLNCE.2018v02i02.004
- Views 68
- Downloads 0
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