Searching for Arbitrary Norm Support Vector Machines information? Find all needed info by using official links provided below.
http://www.cse.cuhk.edu.hk/~lyu/paper_pdf/neco.2008.pdf
Arbitrary Norm Support Vector Machines 561 1 Introduction Asthestate-of-the-artlearningalgorithms,supportvectormachines(SVM) (Vapnik, 2000) have been widely studied and applied in machine learn-ing, pattern recognition, and computer vision. The standard SVM usually adopts a term of L 2-norm or L 1-norm to control the structure complexity,
https://www.researchgate.net/publication/24416831_Arbitrary_Norm_Support_Vector_Machines
Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM ...
https://www.ncbi.nlm.nih.gov/pubmed/19431269
Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L(infinity)-norm SVM, are rarely seen in the literature.Cited by: 12
https://www.mitpressjournals.org/doi/10.1162/neco.2008.12-07-667
Support vector machines (SVM) are state-of-the-art classifiers. Typically L 2-norm or L 1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L 0-norm SVM or even the L ∞-norm SVM, are rarely seen in the literature.The major reason is that L 0-norm describes a discontinuous and nonconvex term, leading to a combinatorially NP-hard optimization problem.Cited by: 12
https://www.orbel.be/workshops/dmor05/DMOR05Carrizosa.pdf
Arbitrary-norm Support Vector Machine. Properties and Applications Emilio Carrizosa Universidad de Sevilla, Spain ... Support Vector Machines: A Data Mining tool In between many fields Machine Learning / Statistics ... [email protected] Arbitrary-norm SVM. Classification problems The Ω-separable case
http://www.cse.cuhk.edu.hk/~lyu/paper_pdf/Arbitrary%20Norm%20Support%20VectorMachines.pdf
Arbitrary Norm Support Vector Machines 3 Figure 1: An illustration of the L 2-norm SVM and the proposed L 0-norm SVM. ×’s and •’s represents two types of data. Samples circled by ’s are the support vectors. The solid lines represent the decision boundaries (when a polynomial
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.615.1837
Abstract. Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L∞-norm SVM, are rarely seen in the literature.
http://jmlr.org/papers/volume10/xu09b/xu09b.pdf
Robustness and Regularization of Support Vector Machines Huan Xu [email protected] Department of Electrical and Computer Engineering 3480 University Street McGill University Montreal, Canada H3A 2A7 Constantine Caramanis [email protected] Department of Electrical and Computer Engineering The University of Texas at Austin 1 University ...
http://jmlr.org/papers/volume7/mangasarian06a/mangasarian06a.pdf
Support vector machines utilizing the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstrained mini-mization of a convex differentiable piecewise-quadratic objective function in the dual space. The
https://papers.nips.cc/paper/2450-1-norm-support-vector-machines.pdf
1-norm Support Vector Machines Ji Zhu, Saharon Rosset, Trevor Hastie, Rob Tibshirani Department of Statistics Stanford University Stanford, CA 94305 {jzhu,saharon,hastie,tibs}@stat.stanford.edu Abstract The standard 2-norm SVM is known for its good performance in two-class classi£cation. In this paper, we consider the 1-norm SVM. We
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