Transfer learning based on logistic regression

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dc.identifier.uri http://dx.doi.org/10.15488/844
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/868
dc.contributor.author Paul, Andreas
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Heipke, Christian
dc.date.accessioned 2016-12-16T09:16:36Z
dc.date.available 2016-12-16T09:16:36Z
dc.date.issued 2015
dc.identifier.citation Paul, A.; Rottensteiner, F.; Heipke, C.: Transfer learning based on logistic regression. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2015), Nr. 3W3, S. 145-152. DOI: https://doi.org/10.5194/isprsarchives-XL-3-W3-145-2015
dc.description.abstract In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method. eng
dc.description.sponsorship DFG/HE 1822/30-1
dc.language.iso eng
dc.publisher Hannover : International Society for Photogrammetry and Remote Sensing
dc.relation.ispartof International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Geospatial Week 2015, 28th September - 3rd October 2015
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2015), Nr. 3W3
dc.rights CC BY 3.0
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject Domain adaptation eng
dc.subject Knowledge transfer eng
dc.subject Logistic regression eng
dc.subject Machine learning eng
dc.subject Remote sensing eng
dc.subject Transfer learning eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.title Transfer learning based on logistic regression
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 16821750
dc.relation.doi https://doi.org/10.5194/isprsarchives-XL-3-W3-145-2015
dc.bibliographicCitation.issue 3W3
dc.bibliographicCitation.volume 40
dc.bibliographicCitation.firstPage 145
dc.bibliographicCitation.lastPage 152
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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