机器学习方面及其在不同研究领域的应用

Yogesh Kumar, K. Kaur, Gurpreet Singh
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引用次数: 30

摘要

机器学习是人工智能的一个分支,旨在通过使用智能软件使机器能够熟练地完成工作。统计学习方法构成了用于开发机器智能的智能软件的支柱。如今,随着大量可用数据集的出现,人们对机器学习的需求大幅增加。利用计算方法从经验改进中获取知识的机械化属于机器学习范畴。专家表现需要特定领域的知识,知识工程已经产生了大量的人工智能专家系统。它的常规使用已经在不同领域的行业中看到。由于机器学习的使用和适用性的增加,本文对与之相关的各个方面进行了系统的综述。本文首先简要介绍了机器学习,以及不同机器学习模型的使用。用于各种目的的各种类型的机器学习算法,如数据挖掘,预测分析,图像处理等,也在综合综述中提出。我们还回顾了不同研究人员在不同应用领域所做的不同工作。它涵盖了机器学习在医疗、社交媒体、旅游和机器人方面的应用。它在不同应用程序中流行背后的主要目的是它能够学习一次,然后自动处理任何相同类型的数据或输入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Aspects and its Applications Towards Different Research Areas
Machine learning is a branch of artificial intelligence that aims at enabling machines to perform their jobs skillfully by using intelligent software. The statistical learning methods constitute the backbone of intelligent software that is used to develop machine intelligence. Now a Day, a huge increase in demand for machine learning has been seen with the great number of available datasets. The knowledge acquisition mechanizing from experience improvement using computational methods comes under machine learning. There is need of knowledge specific to the domain by expert performance and number of AI expert systems has been produced by knowledge engineering. Its regular use has been seen in the industry in different domains. Due to the increase in use and applicability of machine learning, the systematic review on various aspects related to it has been presented in this paper. The paper started with giving a brief description of machine learning, and the use of different models of machine learning. The various types of machine learning algorithms that are used for various purposes like data mining, predictive analytics, image processing etc. has also presented in the comprehensive review. We have also given a review of different work done by various researchers in different application areas. It covered the use of machine learning in medical, social media, Travelling and robotics. The primary purpose behind its popularity in the different application is its ability to learn once and then it works automatically for any same type of data or input given to it.
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