Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Addressing user expectations in mobile content delivery
Mobile Information Systems - Improving Quality of Service in Mobile Information Systems, Services and Networks
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Standardization activities in the ITU for a QoE assessment of IPTV
IEEE Communications Magazine
Zapping delay reduction method for sports live with multi-angle on smart tv
Proceddings of the 9th international interactive conference on Interactive television
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Measuring and predicting the user's Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the user's feedback. Existing statistical techniques have lead to poor accuracy (order of 70%) and inability to evolve prediction models with the system's dynamics. In this paper, we propose a novel approach for building accurate and adaptive QoE prediction models using Machine Learning classification algorithms, trained on subjective test data. These models can be used for real-time prediction of QoE and can be efficiently integrated into online learning systems that can adapt the models according to changes in the environment. Providing high accuracy of above 90%, the classification algorithms become an indispensible component of a mobile multimedia QoE management system.