Multiple Instance Classification via Successive Linear Programming

The multiple instance classification problem (Dietterich et al., Artif. Intell. 89:31–71, [ 1998 ]; Auer, Proceedings of 14th International Conference on Machine Learning, pp. 21–29, Morgan Kaufmann, San Mateo, [ 1997 ]; Long et al., Mach. Learn. 30(1):7–22, [ 1998 ]) is formulated using a linear or...

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Bibliographic Details
Published in:	Journal of optimization theory and applications Vol. 137; no. 3; pp. 555 - 568
Main Authors:	Mangasarian, O. L., Wild, E. W.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.06.2008 Springer Springer Nature B.V
Subjects:	Algorithms Applications of Mathematics Applied sciences Calculus of Variations and Optimal Control; Optimization Classification Classifiers Engineering Exact sciences and technology Integer programming Learning Linear programming Mathematical models Mathematical programming Mathematics Mathematics and Statistics Minimization Nonlinearity Operational research and scientific management Operational research. Management science Operations Research/Decision Theory Optimization Quadratic programming Reduction Studies Support vector machines Theory of Computation Support vector machines Successive linearization algorithm Bilinear constraints Multiple instance learning Kernels Integer programming Learning (artificial intelligence) Vector support machine Minimization Linear programming Classifier Complex programming Multiple instance learning, Support vector machines, Successive linearization algorithm Competitive algorithms Linearization
ISSN:	0022-3239, 1573-2878
Online Access:	Get full text
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