Title :
Analysis of Laplacian Support Vector Machines
Author :
Huang, Juan ; Chen, Hong ; Tao, Yan-Fang
Author_Institution :
Sch. of Math. & Phys., China Univ. of Geosci., Wuhan, China
Abstract :
The goal of semi-supervised learning algorithm is to effectively incorporate labeled and unlabeled data in a general-purpose learner with small misclassification error. Although there are various algorithms to implement semi-supervised learning task, the crucial issue of dependence of generalization error on the number of labeled and unlabeled data is still poorly understood. In this paper, we consider the Laplacian Support Vector Machines (LapSVMs) and establish its error analysis.
Keywords :
error analysis; generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; support vector machines; Laplacian support vector machines; error analysis; generalization error; misclassification error; semisupervised learning algorithm; unlabeled data; Laplace equations; Pattern analysis; Pattern recognition; Support vector machines; Wavelet analysis; LapSVMS; misclassification error; reproducing kernel Hilbert space;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3728-3
Electronic_ISBN :
978-1-4244-3729-0
DOI :
10.1109/ICWAPR.2009.5207440
