Machine Learning
The Vision of Autonomic Computing
Computer
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Creating an Ambient-Intelligence Environment Using Embedded Agents
IEEE Intelligent Systems
How smart are our environments? An updated look at the state of the art
Pervasive and Mobile Computing
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
The Journal of Machine Learning Research
Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
M-PAC '09 Proceedings of the International Workshop on Middleware for Pervasive Mobile and Embedded Computing
Smart Environment Software Reference Architecture
NCM '09 Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC
Managing Adaptive Versatile environments
Pervasive and Mobile Computing
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Learning patterns in ambient intelligence environments: a survey
Artificial Intelligence Review
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Designing smart environments: a paradigm based on learning and prediction
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
A survey on ontologies for human behavior recognition
ACM Computing Surveys (CSUR)
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This work presents a model for using machine learning in the adaptive control of smart environments. The model is based on an investigation of the existing works regarding smart environments and an analysis of the machine learning uses within them. Four different categories of machine learning in smart environments were identified: prediction, recognition, detection and optimisation. These categories can be deployed to different phases of a self-adaptive application utilising the adaptation loop structure. The use of machine learning in one phase of the adaptation loop was demonstrated by carrying out an experiment utilising neural networks in the prediction of latencies.