Welcome to the InfoSci Platform
Reference Hub1
A Combined Dimensional Kernel Method for Graph Classification

A Combined Dimensional Kernel Method for Graph Classification

Tiejun Cao
Copyright: © 2017 |Volume: 10 |Issue: 3 |Pages: 12
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522511892|DOI: 10.4018/JITR.2017070102
Cite Article Cite Article

MLA

Cao, Tiejun. "A Combined Dimensional Kernel Method for Graph Classification." JITR vol.10, no.3 2017: pp.22-33. http://doi.org/10.4018/JITR.2017070102

APA

Cao, T. (2017). A Combined Dimensional Kernel Method for Graph Classification. Journal of Information Technology Research (JITR), 10(3), 22-33. http://doi.org/10.4018/JITR.2017070102

Chicago

Cao, Tiejun. "A Combined Dimensional Kernel Method for Graph Classification," Journal of Information Technology Research (JITR) 10, no.3: 22-33. http://doi.org/10.4018/JITR.2017070102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The data containing structural information is an important problem in the field of machine learning. Kernel methods is an effective technique for solving such problems. A combined dimension kernel method is proposed or graph classification in this paper. A two-dimensional kernel is first constructed in this method, and it incorporates one-dimensional information to characterize the molecular chemistry, and then a three-dimensional kernel is constructed based on the knowledge of molecular mechanics to characterize the physical properties of the molecule. On this basis, the kernel of different dimensions is integrated, and the quadratic programming problem with quadratic constraints is solved to obtain the optimal kernel combination. The experimental results show that the proposed method has better performance than the prior technology, and it outperforms the existing algorithm.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.