A review of supervised machine learning algorithms

By Amanpreet Singh, Narina Thakur, A. Sharma. International Conference on Computing for Sustainable Global Development, 2016

637 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/53b55682222692323a3a0d546d9e1a3de29454f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ji-chao Li
2. Xiaosong Du
3. Joaquim R. Martins
4. Pramila P. Shinde
5. Seema Shah
6. Gaetan K. W. Kenway
7. John T. Hwang
8. Zhoujie Lyu

Citations in the corpus

• A Review of Machine Learning and Deep Learning Applications (2018, methodology): “AdaBoost algorithm shows weakness to over-fitting and outlier instances in the data whereas RF is more robust model [1][3].”
• Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “Supervised Learning Supervised learning models are trained by data sets containing inputs and the corresponding model observations (also called “labels” or “targets”) [117, 118].”