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Activity learning :discovering, recognizing, and predicting human behavior from sensor data /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Activity learning :/ Diane J. Cook, Narayanan C. Krishnan.
Reminder of title:	discovering, recognizing, and predicting human behavior from sensor data /
Author:	Cook, Diane J.,
other author:	Krishnan, Narayanan C.
Published:	Hoboken, NJ :Wiley,c2015.
Description:	xvii, 257 p. :ill. ;25 cm.
Subject:	Active learning - Data processing. -
Online resource:	http://catalogimages.wiley.com/images/db/jimages/9781118893760.jpg
ISBN:	9781118893760 (hardback) :
ISBN:	111889376X (hardback)

Activity learning :discovering, recognizing, and predicting human behavior from sensor data /
Cook, Diane J.,1963-

Activity learning :discovering, recognizing, and predicting human behavior from sensor data /Diane J. Cook, Narayanan C. Krishnan. - Hoboken, NJ :Wiley,c2015. - xvii, 257 p. :ill. ;25 cm. - Wiley series on parallel and distributed computing.

Includes bibliographical references (p. 237-251) and index.

Machine generated contents note: 1 Introduction 2 Activities 2.1 Definitions 2.2 Classes of Activities 2.3 Additional Reading 3 Sensing 3.1 Sensors Used for Activity Learning 3.2 Sample Sensor Datasets 3.3 Features 3.4 Multisensor Fusion 3.5 Additional Reading 4 Machine Learning 4.1 Supervised Learning Framework 4.2 Naïve Bayes Classifier 4.3 Gaussian Mixture Model 4.4 Hidden Markov Model 4.5 Decision Tree 4.6 Support Vector Machine 4.7 Conditional Random Field 4.8 Combining Classifier Models 4.9 Dimensionality Reduction 4.10 Additional Reading 5 Activity Recognition 5.1 Activity Segmentation 5.2 Sliding Windows 5.3 Unsupervised Segmentation 5.4 Measuring Performance 5.5 Additional Reading 6 Activity Discovery 6.1 Zero-Shot Learning 6.2 Sequence Mining 6.3 Clustering 6.4 Topic Models 6.5 Measuring Performance 6.6 Additional Reading 7 Activity Prediction 7.1 Activity Sequence Prediction 7.2 Activity Forecasting 7.3 Probabilistic Graph-Based Activity Prediction 7.4 Rule-Based Activity Timing Prediction 7.5 Measuring Performance 7.6 Additional Reading 8 Activity Learning in the Wild 8.1 Collecting Annotated Sensor Data 8.2 Transfer Learning 8.3 Multi-Label Learning 8.4 Activity Learning for Multiple Individuals 8.5 Additional Reading 9 Applications of Activity Learning 9.1 Health 9.2 Activity-Aware Services 9.3 Security and Emergency Management 9.4 Activity Reconstruction, Expression and Visualization 9.5 Analyzing Human Dynamics 9.6 Additional Reading 10 The Future of Activity Learning Appendix: Sample Activity Data Bibliography.

"The book provides an in-depth look at computational approaches to activity learning from sensor data"--

ISBN: 9781118893760 (hardback) :NT2953

LCCN: 2014039501Subjects--Topical Terms:

1000089653
Active learning
--Data processing.

LC Class. No.: LB1027.23 / .C665 2015

Dewey Class. No.: 371.3

Activity learning :discovering, recognizing, and predicting human behavior from sensor data /
LDR:02661cam a2200277 a 4500 001 1000061708
003 DLC
005 20150731172646.0
008 150115s2015 njua b 001 0 eng
010 $a 2014039501
020 $a 9781118893760 (hardback) : $c NT2953
020 $a 111889376X (hardback)
020 $z 9781119010241 (ePub)
020 $z 9781119010234 (Adobe PDF)
035 $a 2014039501
040 $a DLC $b eng $c DLC $d DLC
042 $a pcc
050 0 0 $a LB1027.23 $b .C665 2015
082 0 0 $a 371.3 $2 23
100 1 $a Cook, Diane J., $d 1963- $3 1000089651
245 1 0 $a Activity learning : $b discovering, recognizing, and predicting human behavior from sensor data / $c Diane J. Cook, Narayanan C. Krishnan.
260 # $a Hoboken, NJ : $b Wiley, $c c2015.
300 $a xvii, 257 p. : $b ill. ; $c 25 cm.
490 0 $a Wiley series on parallel and distributed computing
504 $a Includes bibliographical references (p. 237-251) and index.
505 8 # $a Machine generated contents note: 1 Introduction 2 Activities 2.1 Definitions 2.2 Classes of Activities 2.3 Additional Reading 3 Sensing 3.1 Sensors Used for Activity Learning 3.2 Sample Sensor Datasets 3.3 Features 3.4 Multisensor Fusion 3.5 Additional Reading 4 Machine Learning 4.1 Supervised Learning Framework 4.2 Naïve Bayes Classifier 4.3 Gaussian Mixture Model 4.4 Hidden Markov Model 4.5 Decision Tree 4.6 Support Vector Machine 4.7 Conditional Random Field 4.8 Combining Classifier Models 4.9 Dimensionality Reduction 4.10 Additional Reading 5 Activity Recognition 5.1 Activity Segmentation 5.2 Sliding Windows 5.3 Unsupervised Segmentation 5.4 Measuring Performance 5.5 Additional Reading 6 Activity Discovery 6.1 Zero-Shot Learning 6.2 Sequence Mining 6.3 Clustering 6.4 Topic Models 6.5 Measuring Performance 6.6 Additional Reading 7 Activity Prediction 7.1 Activity Sequence Prediction 7.2 Activity Forecasting 7.3 Probabilistic Graph-Based Activity Prediction 7.4 Rule-Based Activity Timing Prediction 7.5 Measuring Performance 7.6 Additional Reading 8 Activity Learning in the Wild 8.1 Collecting Annotated Sensor Data 8.2 Transfer Learning 8.3 Multi-Label Learning 8.4 Activity Learning for Multiple Individuals 8.5 Additional Reading 9 Applications of Activity Learning 9.1 Health 9.2 Activity-Aware Services 9.3 Security and Emergency Management 9.4 Activity Reconstruction, Expression and Visualization 9.5 Analyzing Human Dynamics 9.6 Additional Reading 10 The Future of Activity Learning Appendix: Sample Activity Data Bibliography.
520 # $a "The book provides an in-depth look at computational approaches to activity learning from sensor data"-- $c Provided by publisher.
650 # 0 $a Active learning $x Data processing. $3 1000089653
650 # 0 $a Detectors $x Data processing. $3 1000089654
650 # 0 $a Multisensor data fusion. $3 164144
700 1 # $a Krishnan, Narayanan C. $3 1000089652
856 4 2 $3 Cover image $u http://catalogimages.wiley.com/images/db/jimages/9781118893760.jpg