A Combined Dimensional Kernel Method for Graph Classification
| Autor: | Tiejun Cao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Graph kernel General Computer Science Computer science business.industry Pattern recognition 02 engineering and technology Kernel principal component analysis 03 medical and health sciences 030104 developmental biology Kernel method Kernel embedding of distributions Graph classification Radial basis function kernel 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Tree kernel business |
| Zdroj: | Journal of Information Technology Research. 10:22-33 |
| ISSN: | 1938-7865 1938-7857 |
| DOI: | 10.4018/jitr.2017070102 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|