FYP16005 Final Year ProjectLearn more
This project aims to study and explore a hypergraph based semi-supervised machine learning algorithm. In this project, the studied algorithm has been implemented. The original binary classification algorithm has been generalised to multi-class classification version. Several activation funcitons have been applied seeking higher accuracy and new regularization function has been substituted for faster convergence and new understanding of the learning process. Parallel computing has been adopted. Multiple experiments have been conducted to examine our hypotheses.
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Research on the sub-gradient approach and total variation approach, understand the mathematical theories
Implement two approaches and perform testing with data from the machine learning repository. (link)
Analyze the deficiency or the limitations of the current methods, plan for further improvements.
Deliverables of interim report Interim presentation rehearsal.
Deliver mathematically our own method according to former analysis. Perform implementation and analyze its accuracy as well as time and space complexity
Deliverables of final report. Final presentation rehearsal.