http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
discovering, recognizing, and predicting human behavior from sensor data
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.
Includes bibliographical references (pages 237-251) and index.
Wiley series on parallel and distributed computing