Abstract
This paper studies the problem of learning with distributions. In this work, we do not focus on the distribution that represents each data point. Instead, we consider the distribution that is an additional information around each data point. The proposed method yields a new kernel that is similar to an existing one. The main difference is that our kernel requires an integration in the kernel space. Theoretically, the proposed method yields a better generalization compared to normal SVM.
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References
Chapelle, O., Haffner, P., Vapnik, V.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 886–893. IEEE Computer Society (2005)
Harchaoui, Z., Bach, F.R.: Image classification with segmentation graph kernels. In: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007). IEEE Computer Society (2007)
Hein, M., Bousquet, O.: Hilbertian metrics and positive definite kernels on probability measures. In: Cowell, R.G., Ghahramani, Z. (eds.) Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, AISTATS. Society for Artificial Intelligence and Statistics (2005)
Jebara, T., Kondor, R., Howard, A.: Probability product kernels. J. Mach. Learn. Res. 5, 819–844 (2004)
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, J.C.H.C.: Text classification using string kernels. J. Mach. Learn. Res. 2, 419–444 (2002)
Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008). IEEE Computer Society (2008)
Moschitti, A.: Making tree kernels practical for natural language learning. In: McCarthy, D., Wintner, S. (eds.) EACL 2006, 11st Conference of the European Chapter of the Association for Computational Linguistics, pp. 113–120. The Association for Computer Linguistics (2006)
Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B.: Learning from distributions via support measure machines. Adv. Neural Inf. Process. Syst. 25, 10–18 (2012)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Sivic, J., Zisserman, A.: Video google: efficient visual search of videos. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 127–144. Springer, Heidelberg (2006)
Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 480–492 (2012)
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Marukatat, S. (2016). Learning with Additional Distributions. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_27
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DOI: https://doi.org/10.1007/978-3-319-42911-3_27
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