Integrating Models of Knowledge and Machine Learning
نویسندگان
چکیده
We propose a theoretical framework allowing a real integration of Machine Learning and Knowledge acquisition. This article shows that the input of a Machine Learning system can be mapped to the model of expertise as it is used in KADS methodology. The notion of learning bias will play a central role. We shall see that parts of it can be identified to what people are used to call the inference and the task models. We shall also see that the description language and its structure, i.e., the background knowledge, can be mainly related to the domain model. Doing this conceptual mapping, we give a status to most of the inputs of Machine Learning programs in terms of knowledge acquisition models. The ENIGME system which implements this work will be presented in this paper. It will show a way to integrate a machine learning algorithm into a knowledge acquisition environment.
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