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Linear and Kernel Classifiers

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Part of the Mathematics in Industry book series (MATHINDUSTRY,volume 37)


Classification is one of the most basic tasks in machine learning. In computer vision, an image classifier is designed to classify input images in corresponding categories. Although this task appears trivial to humans, there are considerable challenges with regard to automated classification by computer algorithms.

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  • DOI: 10.1007/978-981-16-6046-7_2
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  1. S. Boyd, S. P. Boyd, and L. Vandenberghe, Convex optimization. Cambridge University Press, 2004.

    CrossRef  Google Scholar 

  2. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015.

    MathSciNet  CrossRef  Google Scholar 

  3. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2009, pp. 248–255.

    Google Scholar 

  4. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.

    Google Scholar 

  5. V. Vapnik, The nature of statistical learning theory. Springer Science & Business Media, 2013.

    Google Scholar 

  6. B. Schölkopf, A. J. Smola, F. Bach et al., Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, 2002.

    Google Scholar 

  7. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.

    CrossRef  Google Scholar 

  8. H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” in European Conference on Computer Vision (ECCV). Springer, 2006, pp. 404–417.

    Google Scholar 

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Ye, J.C. (2022). Linear and Kernel Classifiers. In: Geometry of Deep Learning. Mathematics in Industry, vol 37. Springer, Singapore.

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