Dear Colleagues,
I am working on a system that performs classification tasks from a large continuous stream of data. This system has a fixed Bayesian network structure and applies an EM algorithm to update its' parameters. There are articles mention incremental learning of Bayesian network. However, my work does not involve any structure changes. My question concerns how I should perform incremental learning for parameters updating with a new data. Given that I have an existing network, - How often should I do for incremental learning (ex. every 10, 100, or 1000). - What is better in terms of accuracy for prediction: 1) learn incrementally with only new data 2) learn every time from the beginning (do batch learning at every k instances). Please feel free to give me any suggestions, comments, or point out to the existing works that I may have overlooked. Sincerely, Parot Ratnapinda
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