NDLI: Partitionable Kernels for Mapping Kernels

Content Provider	IEEE Xplore Digital Library
Author	Kilho Shin
Copyright Year	2011
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.
Starting Page	645
Ending Page	654
File Size	310764
Page Count	10
File Format	PDF
ISBN	9781457720758
ISSN	15504786
DOI	10.1109/ICDM.2011.115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Vegetation Heuristic algorithms Vectors Support vector machines Educational institutions Convolution
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

New heuristic for determination Gaussian kernels parameter

Kernels for One-Class Support Vector Machines

Learning semantic kernels for scene classification

Spanning-Tree Kernels on Graphs

Autocorrelation Kernel Functions for Support Vector Machines

Nonparallel hyperplane support vector machine for PU learning

Modeling label dependencies in kernel learning for image annotation

Optimizing the kernel selection for support vector machines using performance measures

Support vector machines in moderate sub-pixel snow mapping

Partitionable Kernels for Mapping Kernels

Similar Documents

New heuristic for determination Gaussian kernels parameter

Kernels for One-Class Support Vector Machines

Learning semantic kernels for scene classification

Spanning-Tree Kernels on Graphs

Autocorrelation Kernel Functions for Support Vector Machines

Nonparallel hyperplane support vector machine for PU learning

Modeling label dependencies in kernel learning for image annotation

Optimizing the kernel selection for support vector machines using performance measures

Support vector machines in moderate sub-pixel snow mapping

Partitionable Kernels for Mapping Kernels