Hybrid learning in expert networks
| Autor: | Robert Christopher Lacher, S.I. Hruska, D.C. Kuncicky |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Artificial neural network
Knowledge representation and reasoning Process (engineering) business.industry Computer science media_common.quotation_subject Legal expert system Certainty Machine learning computer.software_genre Expert system Intelligent Network Reinforcement learning Algorithm design Artificial intelligence business computer media_common |
| Zdroj: | IJCNN-91-Seattle International Joint Conference on Neural Networks. |
| DOI: | 10.1109/ijcnn.1991.155323 |
| Popis: | Expert networks are defined as the embodiment of an expert's rule-based knowledge in an acyclic feedforward network. A transformation process is used to create an expert network from an expert system to enable training of the certainty factors of the expert system's rules from data. Certainty factors in the expert system correspond to connection weights in the network. The training algorithm presented begins with only the basic architecture of the network and uses a reinforcement learning process to arrive at an improved knowledge state and a back-propagation segment to complete convergence to correct values. Results of a case study illustrate the practicality of the proposed design and of the hybrid learning algorithm used. > |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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