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 sens...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Cook, Diane J. (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Somerset Wiley 2015
Ausgabe:	1st ed
Schriftenreihe:	Wiley Series on Parallel and Distributed Computing
Schlagworte:	Datenverarbeitung Active learning > Data processing Detectors > Data processing Multisensor data fusion Datenauswertung Verhalten Sensorsystem Lernen Maschinelles Lernen
Zusammenfassung:	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
Beschreibung:	Description based on publisher supplied metadata and other sources
Beschreibung:	1 online resource (312 pages)
ISBN:	9781119010241 9781118893760