Supervised Learning

By Weibin Mo, Yufeng Liu. Encyclopedia of Machine Learning and Data Mining, 2021

686 citations recorded by the source corpus.

How researchers see this paper

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

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (2018, methodology): “The model has also been used to describe a simple cellular network with n APs, where each AP serves a single user device [3], [4].”
Fraud detection system: A survey (2016, methodology): “Therefore, unsupervised learning and Semi-supervised learning are used to overcome these disadvantages (Liu and Wu, 2012).”
The Classification of White Wine and Red Wine According to Their Physicochemical Qualities (2016, background): “It was described by Breiman in 2001 [12].”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, background): “The goal of supervised learning algorithm is to learn knowledge instructed by the sample-label pairs [24].”
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, background): “Typical examples of supervised learning applications include weather forecasting, spam detection, face detection, sentiment analysis, pricing predictions, and so on [36].”