Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications (Paperback)

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.

• Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning
• Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering
• Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

《可靠機器學習的符合性預測：理論、適應和應用》是機器學習領域的最新發展，可以在任何現實世界的模式識別應用中，包括風險敏感的應用，如醫學診斷、人臉識別和金融風險預測中，將可靠的信心度與預測相關聯。本書介紹了這個框架的基本理論，演示了如何應用於現實世界的問題，並提出了幾種適應方法，包括主動學習、變化檢測和異常檢測。隨著全球各地的從業者和研究人員應用和適應這個框架，本書匯集了這些研究成果，為進一步研究提供了跳板，同時也是應用於現實世界問題的手冊。

本書的主要內容包括：
- 理解這個重要框架的理論基礎，可以在機器學習中提供可靠的信心度與預測相關聯
- 能夠將這個框架應用於不同的機器學習問題，包括分類、回歸和聚類
- 學習將這個框架有效地適應到新的問題設定中，如主動學習、模型選擇或變化檢測