A Review at Machine Learning Algorithms Targeting Big Data Challenges

被引：0

作者：

Rathor, Abhinav ^{[1
]}

Gyanchandani, Manasi ^{[1
]}

机构：

[1] Maulana Azad Natl Inst Technol, Dept CSE, Bhopal 462003, MP, India

来源：

2017 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNICATION, COMPUTER, AND OPTIMIZATION TECHNIQUES (ICEECCOT) | 2017年

关键词：

Machine Learning; Bigdata; Prediction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Machine learning is an ideal tool for extracting the hidden patterns in the data and making efficient predictions on the same. One of the primary advantages of this paradigm is the minimal dependency on the human factors that make it to deliver its best among disparate and wide variety of sources. It is powered by data running at the machine scale. It is best when the the data is to be dealt with is large in volume, high in speed and diverse in variety. And unlike conventional analysis of data, machine learning thrives with growing data. The more data is entered into a machine, the more it can learn and apply the results for advanced quality insights. The aim of this paper is to present a comparative analysis of the Machine Learning algorithms to best reconcile Big Data challenges drawn on the basis of optimized performance with respect to time and accuracy obtained in prediction.

引用

页码：753 / 758

页数：6

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