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https://people.eng.unimelb.edu.au/shiltona/publications/fusion2002svm.pdf
Distributed Data Fusion Using Support Vector Machines S. Challa Department of Electrical and Electronic Engineering The University of Melbourne Parkville, Victoria 3101 Australia [email protected] M. Palaniswami Department of Electrical and Electronic Engineering The University of Melbourne Parkville, Victoria 3101 Australia [email protected]
https://www.researchgate.net/publication/3959662_Distributed_data_fusion_using_support_vector_machines
Support vector machines, through statistical learning theory, provide a way of compressing information by generating optimal kernal based representations. In this paper we use SVM …
https://ieeexplore.ieee.org/document/1020902/
Distributed data fusion using support vector machines Abstract: The basic quantity to be estimated in the Bayesian approach to data fusion is the conditional probability density function (CPDF). Computationally efficient particle filtering approaches are becoming more important in estimating these CPDFs.Cited by: 25
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.332.1635
In all practical data fusion systems that have limited communication bandwidth, broadcasting this probabilistic information, available as a set of samples, to the fusion center is impractical. Support vector machines, through statistical learning theory, provide a way of compressing information by generating optimal kernal based representations.
https://www.sciencedirect.com/science/article/pii/S0020025512004185
In this article we propose and investigate a hybrid method for fault signal classification based on sensor data fusion by using the Support Vector Machine (SVM) and Short Term Fourier Transform (STFT) techniques. We report a practical application of this hybrid model and evaluate its performance.Cited by: 149
https://www.researchgate.net/publication/228359637_Data_fusion_for_fault_diagnosis_using_multi-class_Support_Vector_Machines
In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to...
https://link.springer.com/article/10.1631/jzus.2005.A1030
Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are processed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models.Cited by: 22
http://bnrg.cs.berkeley.edu/~adj/publications/paper-files/ecml_approx_svm.pdf
processing large-scale data sets using support vector machines (SVM) in con-texts such as distributed networking systems are often prohibitively high, result-ing in practitioners of SVM learning algorithms having to apply the algorithm on approximate versions of the kernel matrix induced by a certain degree of data reduction.
http://www.nt.ntnu.no/users/skoge/prost/proceedings/acc05/PDFs/Papers/0346_ThA06_4.pdf
The distributed data fusion strategies are applied to combine these multi-class support vector machine models.
https://notendur.hi.is/benedikt/waske_TGRS_2007.pdf
Fusion of Support Vector Machines for Classification of Multisensor Data Björn Waske, StudentMember,IEEE, and Jón Atli Benediktsson, Fellow,IEEE Abstract—The classification of multisensor data sets, consisting of multitemporal synthetic aperture radar data and optical im-agery, is addressed. The concept is based on the decision fusion
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