LEADER 03799nam 2200673 450 001 9910132433603321 005 20200520144314.0 010 $a1-119-01025-X 010 $a1-119-01023-3 010 $a1-119-01024-1 035 $a(CKB)3710000000354272 035 $a(EBL)1895908 035 $a(SSID)ssj0001420810 035 $a(PQKBManifestationID)11805034 035 $a(PQKBTitleCode)TC0001420810 035 $a(PQKBWorkID)11404044 035 $a(PQKB)10854631 035 $a(MiAaPQ)EBC1895908 035 $a(DLC) 2015002169 035 $a(Au-PeEL)EBL1895908 035 $a(CaPaEBR)ebr11019388 035 $a(CaONFJC)MIL888541 035 $a(OCoLC)900565386 035 $a(PPN)190567341 035 $a(EXLCZ)993710000000354272 100 $a20150216h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aActivity learning $ediscovering, recognizing, and predicting human behavior from sensor data /$fDiane J. Cook, Narayanan C. Krishnan 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons , Inc.,$d2015. 210 4$dİ2015 215 $a1 online resource (282 p.) 225 1 $aWiley Series on Parallel and Distributed Computing 300 $aDescription based upon print version of record. 311 $a1-118-89376-X 320 $aIncludes bibliographical references and index. 327 $aMachine 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. 330 $a"The book provides an in-depth look at computational approaches to activity learning from sensor data"--$cProvided by publisher. 410 0$aWiley series on parallel and distributed computing. 606 $aActive learning$xData processing 606 $aDetectors$xData processing 606 $aMultisensor data fusion 615 0$aActive learning$xData processing. 615 0$aDetectors$xData processing. 615 0$aMultisensor data fusion. 676 $a371.3 686 $aTEC008060$aTEC064000$aCOM021030$2bisacsh 700 $aCook$b Diane J.$f1963-$0915326 702 $aKrishnan$b Narayanan C. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910132433603321 996 $aActivity learning$92051666 997 $aUNINA