A path algorithm for the support vector domain description and its application to medical imaging

Sjöstrand, Karl; Hansen, Michael Sass; Larsson, Henrik; Larsen, Rasmus

doi:10.1016/j.media.2007.07.008

Cited by 24 publications

(9 citation statements)

References 19 publications

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“…They estimate a decision function that extracts the 'normal' or 'regular' data from of the training sample by maximising the distance of the 'regular' data to the origin. In recent years extensions to this method have been proposed (see for example Bicego & Figueiredo, 2009;Li, 2011;Sjörstrand, Hansen, Larsson, & Larsen, 2007;Tran, Li, & Duan, 2005), and they have been used in various fields (for example audio surveillance Rabaoui, Davy, Rossignol, & Ellouze, 2008; or portfolio selection Gotoh and Takeda, 2012).…”

Section: Related Workmentioning

confidence: 99%

Statistical monitoring for continual quality control of railway ballast

Hofer

Bach²

2015

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

Statistical monitoring for continual quality control of railway ballast

Hofer

Bach²

2015

Expert Systems with Applications

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“…The quality of a given clustering can be assessed using a measure relating the scatter matrix of samples for each cluster and the scatter matrix between clusters [9,10]. However this qualifies the global clustering with the outliers and not only the quality of the clustering of the cores.…”

Section: Clustering Validity Measure (Cvm)mentioning

confidence: 99%

Unsupervised change detection via hierarchical support vector clustering

Morsier

Tuia

Gass

et al. 2012

7th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)

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When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system.

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“…slow convergence, low classification accuracy), which need to be improved. Currently, the SVDD is focused on by the three aspects [18][19][20][21][22][23]: (1) the deformation of the normal SVDD model, such as density-induced SVDD; (2) optimum selection method of training set and increase of computation speed, such as incremental leaning method; and (3) the selection of SVDD parameters, including the kernel function and kernel parameters, such as cross validation and path algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of analog circuit based on a second map SVDD

Jiang

Wang

Luo

2015

Analog Integr Circ Sig Process

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To improve the diagnosis accuracy of analog circuit, this paper presents a second map support vector data description (SM-SVDD) method, which uses an anomalous and close surface instead of a hypesphere to describe the target data. The fault classifier is constructed by a SM SVDD algorithm to realize analog circuit fault diagnosis. Experimental results on two typical circuits confirm that the proposed method is effective in analog circuit fault diagnosis with good accuracy, and the performance surpasses other intelligent diagnosis methods, such as back propagation neural network, support vector machine and the normal SVDD. The developed method can be applied to other multi-classifier in electronic applications.

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A path algorithm for the support vector domain description and its application to medical imaging

Cited by 24 publications

References 19 publications

Statistical monitoring for continual quality control of railway ballast

Statistical monitoring for continual quality control of railway ballast

Unsupervised change detection via hierarchical support vector clustering

Fault diagnosis of analog circuit based on a second map SVDD

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