Gradient Boosting Machine: A Survey
Zhiyuan He, Danchen Lin, Thomas Lau, and Mike Wu
TL;DR
This survey comprehensively reviews various gradient boosting algorithms, detailing their mathematical foundations, optimization techniques, loss functions, model construction, and applications in ranking tasks.
Contribution
It provides a detailed overview of gradient boosting methods, highlighting their mathematical frameworks and diverse applications, which is valuable for researchers and practitioners.
Findings
Different types of gradient boosting algorithms are systematically categorized.
Mathematical frameworks and optimization techniques are thoroughly explained.
Applications in ranking demonstrate the versatility of gradient boosting.
Abstract
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.
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Taxonomy
TopicsMachine Learning and Algorithms · Face and Expression Recognition · Neural Networks and Applications