@inproceedings{f714721300b447ebb0616c554cc15c3a,
title = "Distances and kernels based on cumulative distribution functions",
abstract = "Similarity and dissimilarity measures such as kernels and distances are key components of classification and clustering algorithms. We propose a novel technique to construct distances and kernel functions between probability distributions based on cumulative distribution functions. The proposed distance measures incorporate global discriminating information and can be computed efficiently.",
keywords = "Cumulative Distribution Function, Distance, Kernel, Similarity",
author = "Hongjun Su and Hong Zhang",
note = "Publisher Copyright: {\textcopyright} 2014 CSREA Press.; 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2014, at WORLDCOMP 2014 ; Conference date: 21-07-2014 Through 24-07-2014",
year = "2014",
language = "English",
series = "Proceedings of the 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2014",
publisher = "CSREA Press",
pages = "357--361",
editor = "Arabnia, {Hamid R.} and Leonidas Deligiannidis and Joan Lu and Tinetti, {Fernando G.} and Jane You and George Jandieri and Gerald Schaefer and Solo, {Ashu M. G.}",
booktitle = "Proceedings of the 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2014",
}