Reinforcement and Systemic Machine Learning for Decision Making (Hardcover)

Parag Kulkarni

  • 出版商: IEEE
  • 出版日期: 2012-08-14
  • 定價: $3,980
  • 售價: 9.5$3,781
  • 語言: 英文
  • 頁數: 312
  • 裝訂: Hardcover
  • ISBN: 047091999X
  • ISBN-13: 9780470919996
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

Reinforcement and Systemic Machine Learning for Decision Making

There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines.

The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making.

Chapters include:

  • Introduction to Reinforcement and Systemic Machine Learning
  • Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning
  • Systemic Machine Learning and Model
  • Inference and Information Integration
  • Adaptive Learning
  • Incremental Learning and Knowledge Representation
  • Knowledge Augmentation: A Machine Learning Perspective
  • Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.

商品描述(中文翻譯)

「強化學習和系統機器學習於決策製定」
在讓機器從經驗中學習時,總是會遇到困難。完整的資訊並不總是可得的,或者它會在一段時間內逐漸地以片段的形式出現。對於系統性學習,我們需要了解決策和行動對系統在這段時間內的影響。本書採取了一種整體的方法來滿足這種需求,並提出了一個新的範式-創建新的學習應用程序,最終實現更智能的機器。

作為這個新興領域中首本獨特的書籍,《強化學習和系統機器學習於決策製定》專注於機器學習和系統機器學習這一專門研究領域。它涵蓋了強化學習及其應用、增量式機器學習、重複性失敗修正機制和多角度決策製定等主題。

章節包括:
- 強化學習和系統機器學習簡介
- 整體系統、系統性和多角度機器學習基礎
- 系統性機器學習和模型
- 推理和資訊整合
- 適應性學習
- 增量式學習和知識表示
- 知識擴充:機器學習的觀點
- 構建一個學習系統

這個範式有潛力成為該領域中最常用的範式之一,從事機器和系統學習的專業人士將會發現本書是一個寶貴的資源。