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Some new methods for supervised classification for functional data

Miss Zhu TianmingDepartment of Statistics and Applied Probability, NUS

Date:21 November 2017, Tuesday

Location:S16-05-96, Computer Lab 4

Time:02:00pm - 03:00pm

 

Phd Oral Presentation

Functional data are getting prevalent in many research and industrial fields in recent decades. It is often of interest to classify functional data properly. A number of supervised classification methods have been proposed in the literature. In this thesis, I propose and study three new classifiers for supervised classification for functional data, including a supervised classification method based on functional cosine similarity, a new k-nearest neighbour classifier, and an inverse distance based classifier. Intensive simulation studies and a number of real functional data examples are conducted to demonstrate and illustrate the good performance of the three new functional classifiers via comparing them against several existing competitors.