Introduction to the theory of neural computation
Introduction to the theory of neural computation
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Incremental Learning in SwiftFile
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Automation Policies for Pervasive Computing Environments
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
A note on the utility of incremental learning
AI Communications
Managing and Delivering Context-Dependent User Preferences in Ubiquitous Computing Environments
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Embedding Computational Intelligence in Pervasive Spaces
IEEE Pervasive Computing
SixthSense: a wearable gestural interface
ACM SIGGRAPH ASIA 2009 Art Gallery & Emerging Technologies: Adaptation
Modeling and intelligibility in ambient environments
Journal of Ambient Intelligence and Smart Environments
Managing Adaptive Versatile environments
Pervasive and Mobile Computing
Pervasive Computing in Daidalos
IEEE Pervasive Computing
Perceptron-based learning algorithms
IEEE Transactions on Neural Networks
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Personalization mechanisms often employ behavior monitoring and machine learning techniques to aid the user in the creation and management of a preference set that is used to drive the adaptation of environments and resources in line with individual user needs. This article reviews several of the personalization solutions provided to date and proposes two hypotheses: (A) an incremental machine learning approach is better suited to the preference learning problem as opposed to the commonly employed batch learning techniques, (B) temporal data related to the duration that user context states and preference settings endure is a beneficial input to a preference learning solution. These two hypotheses are the cornerstones of the Dynamic Incremental Associative Neural NEtwork (DIANNE) developed as a tailored solution to preference learning in a pervasive environment. DIANNE has been evaluated in two ways: first, by applying it to benchmark datasets to test DIANNE's performance and scalability as a machine learning solution; second, by end-users in live trials to determine the validity of the proposed hypotheses and to evaluate DIANNE's utility as a preference learning solution.