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What is Handling Concept Drift in Machine Learning about?
This document outlines a tutorial on handling concept drift in machine learning. It discusses the importance of concept drift in real-world data streams and the need for learning models to adapt. The tutorial aims to provide an integrated view of adaptive learning methods and their applications. It presents techniques for handling concept drift, evaluating adaptive approaches, and demonstrating software. The challenges of concept drift in different applications are also examined. The tutorial is divided int
- Author
- Soham Mullick
- Language
- EN