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https://www.researchgate.net/publication/5631990_Weighted_Mahalanobis_Distance_Kernels_for_Support_Vector_Machines
Weighted Mahalanobis Distance Kernels for Support Vector Machines Article in IEEE Transactions on Neural Networks 18(5):1453-62 · October 2007 with 107 Reads How we measure 'reads'
https://ieeexplore.ieee.org/document/4298136/
Weighted Mahalanobis Distance Kernels for Support Vector Machines Abstract: The support vector machine (SVM) has been demonstrated to be a very effective classifier in many applications, but its performance is still limited as the data distribution information is underutilized in determining the decision hyperplane. Most of the existing kernels ...Cited by: 71
https://www.sciencedirect.com/science/article/pii/S0020025515004594
In this paper we proposed and evaluated a new, data dependent kernel function for support vector machines, the responsibility weighted Mahalanobis (RWM) kernel. This kernel considers structure in the data by means of a parametric density modeling approach.Cited by: 24
https://www.ncbi.nlm.nih.gov/pubmed/18220193
Weighted mahalanobis distance kernels for support vector machines. Wang D(1), Yeung DS, Tsang EC. Author information: (1)Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. [email protected] by: 71
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004298136
support vector machines geometry pattern clustering weighted mahalanobis distance kernels agglomerative hierarchical clustering data structure Euclidean distance Euclidean inner product pattern images data distribution information Data structures Support vector machine classification Pattern recognition support vector machines (SVMs) Indefinite ...
https://arxiv.org/abs/1502.04033
Abstract: Kernel functions in support vector machines (SVM) are needed to assess the similarity of input samples in order to classify these samples, for instance. Besides standard kernels such as Gaussian (i.e., radial basis function, RBF) or polynomial kernels, there are also specific kernels tailored to consider structure in the data for similarity assessment.Cited by: 24
https://deepai.org/publication/the-responsibility-weighted-mahalanobis-kernel-for-semi-supervised-training-of-support-vector-machines-for-classification
The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification. 02/13/2015 ∙ by Tobias Reitmaier, et al. ∙ 0 ∙ share . Kernel functions in support vector machines (SVM) are needed to assess the similarity of input samples in order to classify these samples, for instance.
https://dl.acm.org/citation.cfm?id=2825746
The responsibility weighted Mahalanobis (RWM) kernel considers structure information in data with help of a parametric density model.It is perfectly suited for semi-supervised learning as the parameters of the density model can be found in an unsupervised way.For semi-supervised learning the RWM kernel outperforms some other kernel functions including the Laplacian kernel (Laplacian SVM).Cited by: 24
https://www.researchgate.net/publication/272423214_The_Responsibility_Weighted_Mahalanobis_Kernel_for_Semi-Supervised_Training_of_Support_Vector_Machines_for_Classification
The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification Article in Information Sciences · February 2015 with 56 Reads
https://www.youtube.com/watch?v=OmTu0fqUsQk
Jan 25, 2015 · For the Love of Physics - Walter Lewin - May 16, 2011 - Duration: 1:01:26. Lectures by Walter Lewin. They will make you ♥ Physics. Recommended for you
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