Argument based machine learning

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چکیده

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Argument-Based Machine Learning

We present a novel approach to machine learning, called ABML (argumentation based ML). This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert’s arguments, or reasons, for some of the learning examples. We require that the theory induced from the examples explains the examples in terms of the given reasons. Thus argument...

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Elicitation of neurological knowledge with argument-based machine learning

OBJECTIVE The paper describes the use of expert's knowledge in practice and the efficiency of a recently developed technique called argument-based machine learning (ABML) in the knowledge elicitation process. We are developing a neurological decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The...

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Argument Based Machine Learning (ABML) is a new approach to machine learning in which the learning examples could be accompanied by arguments. The arguments for specific examples are a special form of expert’s background knowledge which the expert uses to substantiate the class value for the chosen example. Možina et al. developed ABCN2 algorithm an extension of a well known rule learning algor...

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Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning

Knowledge elicitation is known to be a difficult task and thus a major bottleneck in building a knowledge base. Machine learning has long ago been proposed as a way to alleviate this problem. Machine learning usually helps the domain expert to uncover some of the more tacit concepts. However, the learned concepts are often hard to understand and hard to extend. A common view is that a combinati...

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ژورنال

عنوان ژورنال: Artificial Intelligence

سال: 2007

ISSN: 0004-3702

DOI: 10.1016/j.artint.2007.04.007