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dc.contributor.authorZuo, Hongming-
dc.description.abstractA self-­‐learning Audio Player was built to learn users habits by analyzing operations the user does when listening to music. The self-­‐learning component is intended to provide a better music experience for the user by generating a special playlist based on the prediction of users favorite songs. The rough set core characteristics are used throughout the learning process to capture the dynamics of changing user interactions with the audio player. The engine is evaluated by simulation data. The simulation process ensures the data contain specific predetermined patterns. Evaluation results show the predictive power and stability of the hybrid engine for learning a users habits and the increased intelligence achieved by combining rough sets and NN when compared with using NN by itself.en_CA
dc.publisherLaurentian University of Sudburyen_CA
dc.subjectself learningen_CA
dc.subjectArtificial Neural Networken_CA
dc.titleA self-learning audio player that uses a rough set and neural net hybrid approachen_CA
dc.description.degreeMaster of Science (MSc) in Computational Sciencesen_CA
dc.publisher.grantorLaurentian University of Sudburyen_CA
Appears in Collections:Master's theses
Master's Theses

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