Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Partitionable Kernels for Mapping Kernels

Authors: Kilho Shin;

Partitionable Kernels for Mapping Kernels

Abstract

Many of tree kernels in the literature are designed tanking advantage of the mapping kernel framework. The most important advantage of using this framework is that we have a strong theorem to examine positive definiteness of the resulting tree kernels. In the mapping kernel framework, each data object is viewed as a collection of components, and a mapping kernel for a pair of data objects is determined as a sum of kernel values of component pairs over a certain range determined according to the purpose of use of the resulting mapping kernel. For those tree kernels known to belong to the mapping kernel category, the string kernel of the product type is commonly used to compute the kernel values of component pairs. This is because it is known that use of the product-type string kernel together with the mapping kernel framework allows us to have recursive formulas to calculate the resulting tree kernels efficiently. We significantly generalizes this result. In fact, we show that we can use partition able kernels, a new class of string kernels instead of the product-type string kernel to enjoy the same advantage, that is, efficient computation based on recursive formulas. The class of partition able kernels is abundant, and contains the product-type string kernels just as an instance. Also, this result, not limited to tree kernels, can be applied to general mapping kernels after we formalize the decomposition properties of trees as the new notion of pretty decomposability.

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!