Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/138153
Title: SOME NEW METHODS FOR SUPERVISED CLASSIFICATION FOR FUNCTIONAL DATA
Authors: ZHU TIANMING
Keywords: functional data analysis, supervised classification, cosine similarity, centroid classifier, k-nearest neighbor classifier, inverse distance,
Issue Date: 16-Aug-2017
Citation: ZHU TIANMING (2017-08-16). SOME NEW METHODS FOR SUPERVISED CLASSIFICATION FOR FUNCTIONAL DATA. ScholarBank@NUS Repository.
Abstract: 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 neighbor 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.
URI: http://scholarbank.nus.edu.sg/handle/10635/138153
Appears in Collections:Ph.D Theses (Open)

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