BROWSE
Detail
Conference Paper
A Design of Continuous Learning System based on Knowledge Augmentation
Cited 0 time in 
Download 1 time
Share 
Download 1 time
Share
- Authors
- Hyunjoong Kang, Soon Hyun Kwon, Eun Joo Kim, HyunJae Kim, Ho Sung Lee, Kwihoon Kim, Nae-soo Kim
- Issue Date
- 2017-02
- Citation
- International Conference on Platform Technology and Service (PlatCon) 2017, pp.1-4
- Language
- English
- Type
- Conference Paper
- Abstract
- To create an algorithm with Machine Learning, users should understand all the knowledge such as learning rate, activation, dimension reduction, hyper parameter, neural network, etc. Therefore, in order to construct a machine learning procedure, expert knowledge is required. So, it is difficult for general users to use it. Also, experts are also hard to regenerate well-defined model if it is described only in the paper. In this paper, we propose a knowledge based Continuous Learning System (CLS) which persistently collect and infer new knowledge from information for the existing learning setup and results instantiated based on a hierarchically designed ontology model.
- KSP Keywords
- Continuous learning, Dimension Reduction, Knowledge augmentation, Knowledge-based, Learning System, Learning procedure, Learning rate, Ontology Model, Well-defined, expert knowledge, machine Learning