Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data (Hardcover)
暫譯: 活動學習：從感測器數據中發現、識別和預測人類行為 (精裝版)

Diane J. Cook, Narayanan C. Krishnan

Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data (Hardcover)

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商品描述

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field

Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following:

  • Discovering activity patterns that emerge from behavior-based sensor data
  • Recognizing occurrences of predefined or discovered activities in real time
  • Predicting the occurrences of activities

The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use.

With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

商品描述(中文翻譯)

定義從感測器數據學習的活動模型概念，並介紹構成該領域核心的關鍵演算法

活動學習：從感測器數據發現、識別和預測人類行為深入探討了從感測器數據進行活動學習的計算方法。每一章節都旨在提供實用的逐步資訊，說明如何分析和處理感測器數據。本書討論的活動學習技術包括以下內容：


  • 發現基於行為的感測器數據中出現的活動模式

  • 實時識別預定義或發現的活動的發生

  • 預測活動的發生

所涵蓋的技術可應用於多個領域，包括安全、電信、醫療保健、智慧電網和家庭自動化。一本線上伴隨網站使讀者能夠實驗書中描述的技術，並根據自己的需求調整或增強這些技術。



強調計算方法的活動學習：從感測器數據發現、識別和預測人類行為為研究生和研究人員提供了活動學習的演算法視角。

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