A Review on Various Algorithms used in Machine Learning-Reference-Cited by-同舟云学术

A Review on Various Algorithms used in Machine Learning

Published:2019-04-03 Issue: Volume: Page:915-920
ISSN:2456-3307
Container-title:International Journal of Scientific Research in Computer Science, Engineering and Information Technology
language:en
Short-container-title:IJSRCSEIT

Author:

Chaudhary Divya¹,Vasuja Er. Richa²

Affiliation:

1. Computer Science and Engineering, Department, Chandigarh University, Gharuan, Punjab, India

2. Assistant Professor, Computer Science and Engineering, Department, Chandigarh University, Gharuan, Punjab, India

Abstract

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.

Publisher

Technoscience Academy

Subject

General Medicine

Reference9 articles.

1. Pat Langley, Stanford and Herbert A.Simon, Pittsburgh. "Application of Machine Learning and Rule Induction." available at http://cll.stanford.edu/~langley/papers/app.cacm.ps.

2. C. J. Burges, “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, vol. 2, (1998), pp. 1-43

3. M. Li and al. “An improved k-means algorithm based on Map reduce and Grid”, International Journal of Grid Distribution Computing, (2015)

4. C. J. Burges, “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, vol. 2, (1998), pp. 1-43

5. Klosgen, W. and Zytkow, J.M. 1996. Knowledge discovery in databases terminology. Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), AAAI Press/The MIT Press, pp. 573–592

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data;Agricultural Science and Technology;2024-06

2. A neural network framework for predicting Adenocarcinoma cancer using high-throughput gene expression data;AIP Conference Proceedings;2023

3. Random Forest Based Legal Prediction System;Algorithms for Intelligent Systems;2022