基于大数据的机器学习算法在各个领域的系统分析

Yang Wang, Tianding Zhou, Chenglin Li, Zhixin Liu, Shichuang Zheng, Qingqing Liu
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引用次数: 0

摘要

机器学习是一项基于数据预测结果的基本创新。本文主要介绍了四种著名的大数据机器学习算法:贝叶斯决策理论分类算法和线性回归算法。基础的两个是监督学习算法,第三个是独立学习算法,第四个是关系算法。机器学习的优势集成了灵活性和适应性差异化和习惯的生物统计方法,这使得它可以部署到某些任务中,例如下注划分,寻找和收集,以及毅力假设。机器学习算法的另一个好处是能够检查各种数据类型。每一种方法都经过广泛的探索和检验。同样,重点放在这四种技术如何相互处理,以激发对更强大、更精通数据的算法的研究。最后,该综述描述了障碍,讨论了研究困难,并建议了未来推动机器学习数据熟练程度考试的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic Analysis of Big Data Based Machine Learning Algorithms on Various Fields
Machine Learning is a basic innovation in foreseeing results in view of data. Four famous Big Data machine learning algorithms are tended to in this paper: Bayesian Decision Theory Classification, and Linear Regression. The underlying two are overseen learning algorithms, the third an independent learning algorithm, and the fourth a relationship algorithm. Advantages of machine learning integrate flexibility and adaptability differentiated and customary biostatistical methods, which makes it deployable for certain tasks, similar to bet partition, finding and gathering, and perseverance assumptions. One more benefit of machine learning algorithms is the capacity to examine assorted data types. Every methodology is broadly explored and examined. Likewise, the accentuation is placed on how the four techniques transaction with one another to rouse investigation of more strong and data-proficient algorithms. At long last, the review portrays the impediments, talks about research difficulties, and recommends future chances to propel the examination on data-proficiency in machine learning.
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