• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.415-425
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• 2000

We proposed a new classification algorithm based on bootstrap sampling and pairwise coupling method. Also, for comparing the accuracy of a proposed algorithm with those of old methods, we conducted classification with waveform data and others.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.635-644
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• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.