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Main Authors: Szedmak, Sandor, Bach, Eric
Format: Preprint
Published: 2020
Subjects:
Online Access:https://arxiv.org/abs/2007.05943
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author Szedmak, Sandor
Bach, Eric
author_facet Szedmak, Sandor
Bach, Eric
contents The Tanimoto kernel (Jaccard index) is a well known tool to describe the similarity between sets of binary attributes. It has been extended to the case when the attributes are nonnegative real values. This paper introduces a more general Tanimoto kernel formulation which allows to measure the similarity of arbitrary real-valued functions. This extension is constructed by unifying the representation of the attributes via properly chosen sets. After deriving the general form of the kernel, explicit feature representation is extracted from the kernel function, and a simply way of including general kernels into the Tanimoto kernel is shown. Finally, the kernel is also expressed as a quotient of piecewise linear functions, and a smooth approximation is provided.
format Preprint
id arxiv_https___arxiv_org_abs_2007_05943
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle On the generalization of Tanimoto-type kernels to real valued functions
Szedmak, Sandor
Bach, Eric
Machine Learning
The Tanimoto kernel (Jaccard index) is a well known tool to describe the similarity between sets of binary attributes. It has been extended to the case when the attributes are nonnegative real values. This paper introduces a more general Tanimoto kernel formulation which allows to measure the similarity of arbitrary real-valued functions. This extension is constructed by unifying the representation of the attributes via properly chosen sets. After deriving the general form of the kernel, explicit feature representation is extracted from the kernel function, and a simply way of including general kernels into the Tanimoto kernel is shown. Finally, the kernel is also expressed as a quotient of piecewise linear functions, and a smooth approximation is provided.
title On the generalization of Tanimoto-type kernels to real valued functions
topic Machine Learning
url https://arxiv.org/abs/2007.05943