Proceedings Article10.1109/ICCIAUTOM.2011.6183968
A classification algorithm for multi-classes based on SVM
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TL;DR: An improved SVM algorithm to solve the K-category classification problem by using one-from-the-rest (OFR) separation for each class and experimental results show that the novel approach is prior to the proximal SVM.
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Abstract: Support vector machine (SVM) constructs an optimal hyperplane utilizing a small set of vectors near boundary. The proximal SVM is an extremely simple procedure to generate linear and nonlinear classifier based on proximity to one of two parallel planes that are separated as far as possible. However, when the two-class are very unbalanced, the proximal SVM tends to fit better the class with more samples and has higher error in fewer samples. Further more, this draw back exists in K-category classification by using one-from-the-rest (OFR) separation for each class. To solve the problem, an improved SVM algorithm is presented in this paper. Experimental results show that the novel approach is prior to the proximal SVM.
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Citations
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COAT: COnstraint-based Anonymization of Transactions
Grigorios Loukides,Aris Gkoulalas-Divanis,Bradley A. Malin +2 more
- 13 Dec 2009
TL;DR: COnstraint-based Anonymization of Transactions (COAT) as mentioned in this paper is the first approach for anonymizing transactional data under application-specific privacy and utility requirements, which is also shown to be effective in a real-world scenario that requires disseminating patients’ information.
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References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Proximal support vector machine classifiers
Glenn Fung,Olvi L. Mangasarian +1 more
- 26 Aug 2001
TL;DR: Computational results on publicly available datasets indicate that the proposed proximal SVM classifier has comparable test set correctness to that of standard S VM classifiers, but with considerably faster computational time that can be an order of magnitude faster.
Multisurface proximal support vector machine classification via generalized eigenvalues
Olvi L. Mangasarian,E.W. Wild +1 more
TL;DR: Tests on simple examples as well as on a number of public data sets show the advantages of the proposed approach in both computation time and test set correctness.
COAT: COnstraint-based anonymization of transactions
TL;DR: COnstraint-based Anonymization of Transactions is proposed, an algorithm that anonymizes transactions using a flexible anonymization scheme to meet the specified constraints and is shown to be effective in preserving both privacy and utility in a real-world scenario that requires disseminating patients’ information.
Generalized Support Vector Machines
Olvi L. Mangasarian
- 01 Jan 1998
TL;DR: By setting apart the two functions of a support vector machine: separation of points by a nonlinear surface in the original space of patterns, and maximizing the distance between separating planes in a higher dimensional space, this work is able to deene indeenite, possibly discontinuous, kernels, not necessarily inner product ones, that generate highly nonlin-ear separating surfaces.