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Some new methods for supervised classification for functional data
Miss Zhu Tianming
Department of Statistics and Applied Probability, NUS
Tuesday 21 November 2017, 02:00pm - 03:00pm
S16-05-96, Computer Lab 4

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.