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https://www-users.cs.umn.edu/~boley/publications/papers/ClusterSVM.pdf
Training Support Vector Machine using Adaptive Clustering Daniel Boley Dongwei Caoy Abstract Training support vector machines involves a huge optimiza-tion problem and many specially designed algorithms have been proposed. In this paper, we proposed an algorithm called ClusterSVM that accelerates the training process by
https://www.researchgate.net/publication/228824563_Training_Support_Vector_Machine_using_Adaptive_Clustering
Clustering support vector machines (ClusterSVM) partition the training data into disjoint clusters first, then train an initial support vectors using representatives of these clusters.
https://www.researchgate.net/publication/220907212_Training_Support_Vector_Machines_Using_Adaptive_Clustering
Using a large national health database, we propose an enhanced SVM-based model called Hierarchical Clustering Support Vector Machine (HCSVM) that utilizes multiple levels of clusters to classify ...
https://experts.umn.edu/en/publications/training-support-vector-machine-using-adaptive-clustering
Training support vector machines involves a huge optimization problem and many specially designed algorithms have been proposed. In this paper, we proposed an algorithm called CluaterSVM that accelerates the training process by exploiting the distributional properties of the training data, that is, the natural clustering of the training data and the overall layout of these clusters relative to ...Cited by: 113
https://www-users.cs.umn.edu/~boley/publications/papers/ICDM03.pdf
Training Support Vector Machines via Adaptive Clustering Daniel Boley∗ Dongwei Cao† Abstract Training support vector machines involves a huge optimiza-tion problem and many specially designed algorithms have been proposed. In this paper, we proposed an algorithm called ClusterSVM that accelerates the training process by
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.215.2848
Abstract. Training support vector machines involves a huge optimization problem and many specially designed algorithms have been proposed. In this paper, we proposed an algorithm called ClusterSVM that accelerates the training process by exploiting the distributional properties of the training data, that is, the natural clustering of the training data and the overall layout of these clusters ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.8549
Abstract. Training support vector machines involves a huge optimization problem and many specially designed algorithms have been proposed. In this paper, we proposed an algorithm called ClusterSVM that accelerates the training process by exploiting the distributional properties of the training data, that is, the natural clustering of the training data and the overall layout of these clusters ...
https://epubs.siam.org/doi/abs/10.1137/1.9781611972740.12
Proceedings of the 2004 SIAM International Conference on Data Mining > ... Proceedings of the 2004 SIAM International Conference on Data Mining Manage this Paper. Add to my favorites. Download Citations. Track Citations. Recommend & Share. ... Projection-Based Model Order Reduction Methods for the Estimation of Vector-Valued Variables of Interest
https://en.wikipedia.org/wiki/Support-vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences.
https://www.cs.cornell.edu/people/tj/publications/finley_joachims_05a.pdf
Supervised Clustering with Support Vector Machines Thomas Finley [email protected] Thorsten Joachims [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14853 USA Abstract Supervised clustering is the problem of train-ing a clustering algorithm to produce desir-able clusterings: given sets of items and com-
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