Name
Abstract | ||
|---|---|---|
A novel framework and method to generate automatically deep learning (DL) services for developers who are not artificial intelligence (AI) experts is presented. Two issues have been considered for the framework: 1) separation of the knowledge on DL from DL generation engines, and 2) cooperation between Automatic Service Composition (ASC) and the CRoss-Industry Standard Process for Data Mining (CRISP-DM) procedure. First, the separation of knowledge on DL from DL generation engines is necessary to adapt to advances and changes in DL technology. Ontology and rules for knowledge and experience in regard to DL technology applications will therefore be constructed for DL generation engines. Second, a framework to compose a target service for generation of a DL architecture requested by non-AI domain experts will be constructed using ASC that works with input involving user requirements based on the CRISP-DM procedure. The created ontology, rules, and composition procedure based on a scenario for special document classification are explained. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICWS.2019.00078 | 2019 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
Deep Learning Generation, Automatic Service Composition, Ontology and Rules, CRISP-DM, Technology Adaptation | Document classification,Ontology,Architecture,Software engineering,Computer science,Service composition,Artificial intelligence,Deep learning,User requirements document,Database | Conference |
ISBN | Citations | PageRank |
978-1-7281-2718-7 | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Incheon Paik | 1 | 241 | 38.80 |
| Ryo Ataka | 2 | 0 | 0.34 |