Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21993
Title: Partially supervised classification - based on weighted unlabeled samples support vector machine
Authors: Liu, Z
Shi, W 
Li, D
Qin, Q
Issue Date: 2005
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2005, v. 3584 LNAI, p. 118-129 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: This paper addresses a new classification technique: partially supervised classification (PSC), which is used to identify a specific land-cover class of interest from a remotely sensed image by using unique training samples belong to a specifically selected class. This paper also presents and discusses a novel Support Vector Machine (SVM) algorithm for PSC. Its training set includes labeled samples belong to the class of interest and unlabeled samples of all classes randomly selected from a remotely sensed image. Moreover, all unlabeled samples are assumed to be training samples of other classes and each of them is assigned a weighting factor indicating the likelihood of this assumption; hence, the algorithm is so-called 'Weighted Unlabeled Sample SVM' (WUS-SVM). Experimental results with both simulated and real data sets indicate that the proposed PSC method is more robust than 1-SVM and has comparable accuracy to a standard SVM.
Description: 1st International Conference on Advanced Data Mining and Applications, ADMA 2005, Wuhan, 22-24 July 2005
URI: http://hdl.handle.net/10397/21993
ISBN: 354027894X
9783540278948
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

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