Machine Learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining
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
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
New options for hoeffding trees
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Video quality measurement standards: current status and trends
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Stress-testing hoeffding trees
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Standardization activities in the ITU for a QoE assessment of IPTV
IEEE Communications Magazine
Can Skype be used beyond video calling?
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Quality of experience management for video streams: the case of Skype
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Instantaneous Video Quality Assessment for lightweight devices
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience QoE is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.