Gradient Boosting Machine: A Survey
arXiv:1908.06951v153 citations
AI Analysis
It provides a comprehensive overview for researchers and practitioners in machine learning, but is incremental as it synthesizes existing knowledge without new findings.
This survey reviews various gradient boosting algorithms, detailing their mathematical frameworks including objective function optimization, loss function estimations, and model constructions, with applications in ranking.
In this survey, we discuss several different types of gradient boosting algorithms and illustrate their mathematical frameworks in detail: 1. introduction of gradient boosting leads to 2. objective function optimization, 3. loss function estimations, and 4. model constructions. 5. application of boosting in ranking.