Knowledge-based Systems
By Md. Shahriare Satu, Md. Imran Khan, Mufti Mahmud, S. Uddin, Matthew A. Summers, Julian M. W. Quinn, Mohammed Ali Moni. 2009
1,262 citations recorded by the source corpus.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jiliang Tang
- Jun Zhou
- Thippa Reddy Gadekallu
- Adel Hatami-Marbini
- Long Xia
- Stewart Massie
- Gokul Yenduri
- Madjid Tavana
Citations in the corpus
- A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011, methodology): “Kao and Liu (2007) proposed a modification to the Kao and Liu's (2000b) method to handle missing values.”
- Towards Explainable Text Classification by Jointly Learning Lexicon and Modifier Terms (2017, background): “Some lexicons also contain additional contextual information in order to help their users build more complex models [Muhammad et al., 2016] as well as a list of terms which modify the strength (intensifiers, diminishers) or the polarity (negators) of other terms within a predetermined window.”
- Deep reinforcement learning for page-wise recommendations (2018, background): “Knowledge-based systems[1] recommend items based on specific domain knowledge about how certain item features meet users’ needs and preferences and how the item is useful for the user.”
- Deep Reinforcement Learning for List-wise Recommendations (2017, background): “Knowledge-based systems[1] recommend items based on specific domain knowledge about how certain item features meet usersâĂŹ needs and preferences and how the item is useful for the user.”
- Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions (2021, methodology): “In general, it consists of two major steps: local learning and model transmission which enables attainment of privacy preservation and cost reduction which are expected in traditional centralized machine learning methods [Zhang et al.(2021)Zhang, Xie, Bai, Yu, Li, and Gao].”
- Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018, background): “Knowledge-based systems[1] recommend items based on specific domain knowledge about how certain item features meet users needs and preferences and how the item is useful for the user.”