Gaussian process regression for tool wear prediction

By Dongdong Kong, Yongjie Chen, Ning Li. 2018

306 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c86aa865995183b7b5ede04139686b6282e3f5a1

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Bernardo Pérez Orozco
2. Stephen J. Roberts
3. Gabriele Abbati

In Mechanical Engineering

1. Bernardo Pérez Orozco
2. Stephen J. Roberts
3. Gabriele Abbati

Citations in the corpus

• MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting (2018, methodology): “Consequently, the time series literature has increasingly been dominated by models such as the Gaussian Process [5, 6, 7, 8, 9] which enable the inference of full predictive distributions in a principled manner, even in the presence of little data.”