Sequence prediction using recurrent neural network
The project implemented a Gap-Filling Engine capable of filling in gaps in missing sequences of various types. A strategy was introduced to look forward into subsequent data, enabling the Engine to improve the accuracy of the prediction by more than 30%. A Sequence Model based on Long Short-term Mem...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2017
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| Online Access: | http://hdl.handle.net/10356/70503 |