Domain Knowledge Assimilation by Learning Complex Concepts
Identifieur interne : 000C43 ( Main/Exploration ); précédent : 000C42; suivant : 000C44Domain Knowledge Assimilation by Learning Complex Concepts
Auteurs : Trung Nguyen [Pologne]Source :
- Lecture Notes in Computer Science [ 1861-2059 ] ; 2008.
Abstract
Abstract: Domain, or background, knowledge has proven to be a key component in the development of high-performance classification systems, especially when the objects of interest exhibit complex internal structures, as in the case of images, time series data or action plans. This knowledge usually comes in extrinsic forms such as human expert advices, often contains complex concepts expressed in quasi-natural descriptive languages and need to be assimilated by the classification system. This paper presents a framework for the assimilation of such knowledge, equivalent to matching different ontologies of complex concepts, using rough mereology theory and rough set methods. We show how this framework allows a learning system to acquire complex, highly structured concepts from an external expert in an intuitive and fully interactive manner. We also argue the needs to focus on expert’s knowledge elicited from outlier or novel samples, which we deem have a crucial impact on the classification process. Experiment results show that the proposed methods work well on a large collection of handwritten digits, though they are by no means limited to this particular type of data.
Url:
DOI: 10.1007/978-3-540-85064-9_10
Affiliations:
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