Distributed Data Fusion Using Support Vector Machines

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Distributed Data Fusion Using Support Vector Machines

    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]

(PDF) Distributed data fusion using support vector machines

    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 …

Distributed data fusion using support vector machines ...

    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

Distributed Data Fusion Using Support Vector Machines

    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.

Multi-sensor data fusion using support vector machine for ...

    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

Data fusion for fault diagnosis using multi-class Support ...

    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...

Data fusion for fault diagnosis using multi-class Support ...

    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

Support vector machines, data reduction, and approximate ...

    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.

Fusion of Multi-Class Support Vector Machines for Fault ...

    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.

Fusion of Support Vector Machines for Classification of ...

    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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