International Journal of Smart Home · 2015 · 23 citations · 10 references
ComputingComputer ScienceData MiningMachine LearningRecommender SystemsContext-aware ComputingPersonalizationRecommendation SystemsSmart Building TechnologiesK-nearest NeighborsMahalanobis DistanceMahalanobis Distance AnalysisPredictive Service ModelingSmart Home ApplicationsUser ContextUser Context InferenceUser Context Modeling
Context-awareness is an important characteristic of smart home. Several methods are used in context-aware application to provide services. The main target of smart home is to predict the demand of home users and proactively provide the proper services by computing user’s context information. In this paper, we present a context-aware application which can provide service according to predefined choice of user. It uses Mahalanobis distance based k nearest neighbors classifier technique for inference of predefined service. We combine the features of supervised and unsupervised machine learning in the proposed application. This application can also adapt itself when the choice of user is changed by using Q-learning reinforcement learning algorithm.
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Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks · 2005 · 25.8K citations