DocumentCode
3019914
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
fYear
2009
fDate
12-15 July 2009
Firstpage
128
Lastpage
132
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ICWAPR.2009.5207440
Filename
5207440
Link To Document