The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Intelligent Agents Meet the Semantic Web in Smart Spaces
IEEE Internet Computing
CASS: A Context-Aware Simulation System for Smart Home
SERA '07 Proceedings of the 5th ACIS International Conference on Software Engineering Research, Management & Applications
OntoCBR: Ontology-Based CBR in Context-Aware Applications
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
Adaptive data aggregation scheme in clustered wireless sensor networks
Computer Communications
Hierarchical SVM Classification for Localization in Multilevel Sensor Networks
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
CCBR: Chaining Case Based Reasoning in Context-Aware Smart Home
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
A compiler for the smart space
AmI'07 Proceedings of the 2007 European conference on Ambient intelligence
A Context-Aware User Interface for Wireless Personal-Area Network Assistive Environments
Wireless Personal Communications: An International Journal
Priority-Oriented Architecture Service Management on OSGi Home-service Platform
Wireless Personal Communications: An International Journal
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The intelligent smart home provides homeowners with various services that incorporate knowledge reasoning. However, programmers must consider such constraints as scenarios in different houses, scenarios of different users, and even different resources. That is infrastructure deployment and scenario designing are time consuming. Actually, designing a home based on the behaviors of family members is more reasonable compare with having users adapt to the functionalities of a home. Therefore, developing an efficient reasoning system for smart homes has gained considerable attention. This work presents a smart home reasoning system called the adaptive scenario-based reasoning (ASBR) system. This system learns from user preferences using adaptive history scenarios and it is a convenient method for rebuilding reasoned knowledge compare with other smart homes. Ontology based contextual information is able to be extracted from a smart home and considered as a set of scenarios. Additionally, the system derives personalized habits and store in web ontology language (OWL) files. This work then presents a novel scenario reconstruction method under computational and resource restriction. Finally, an experiment is designed for a realistic smart home and some scenarios are used to discuss the results.