Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications (Paperback)
暫譯: 可靠機器學習的符合預測:理論、調整與應用 (平裝本)

Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

  • 出版商: Morgan Kaufmann
  • 出版日期: 2014-04-29
  • 定價: $3,650
  • 售價: 8.0$2,920
  • 語言: 英文
  • 頁數: 334
  • 裝訂: Paperback
  • ISBN: 0123985374
  • ISBN-13: 9780123985378
  • 相關分類: Machine Learning
  • 立即出貨 (庫存 < 3)

商品描述

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

商品描述(中文翻譯)

符合預測框架是機器學習中的一項新發展,能夠在任何現實世界的模式識別應用中,為預測提供可靠的信心度量,包括風險敏感的應用,如醫療診斷、臉部識別和金融風險預測。《可靠機器學習的符合預測:理論、適應與應用》捕捉了該框架的基本理論,展示了如何將其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。隨著全球的實踐者和研究人員應用和調整該框架,這本編輯卷匯集了這些研究成果,為進一步研究提供了跳板,也為現實世界問題的應用提供了手冊。

- 理解這一重要框架的理論基礎,該框架能夠為機器學習中的預測提供可靠的信心度量
- 能夠將此框架應用於不同機器學習環境中的現實世界問題,包括分類、回歸和聚類
- 學習有效的方法將該框架適應於更新的問題環境,如主動學習、模型選擇或變化檢測