Gradient boosting machines, a tutorial
Alexey Natekin low, Alois Knoll
Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods with a strong focus on machine learning aspects of modeling. A theoretical information is complemented with descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. Three practical examples of gradient boosting applications are presented and comprehensively analyzed.
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What cites it, inside the corpus
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Topics
| Machine Learning and ELM | Computer Science |
| Neural Networks and Applications | Computer Science |
| Face and Expression Recognition | Computer Science |
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