Incremental learning of retrieval knowledge in a case-based reasoning system

Authors:
Petra Perner
Affiliations:
Institute of Computer Vision and Applied Computer Sciences, Leipzig
Venue:
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Year:
2003

Citing 11
Cited 6

Models of incremental concept formation

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Reasoning with complex cases

Reasoning with complex cases
Similarity Measures for Structured Representations

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Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning

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A Two Layer Case-Based Reasoning Architecture for Medical Image Understanding

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Structural Similarity and Adaptation

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Structured Cases, Trees and Efficient Retrieval

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Similarity Measures for Object-Oriented Case Representations

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Introduction

Case-Based Reasoning Technology, From Foundations to Applications
Categorizing Case-Base Maintenance: Dimensions and Directions

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning

Prototypical case mining from biomedical literature for bootstrapping a case base

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Concise case indexing of time series in health care by means of key sequence discovery

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Case-based retrieval to support the treatment of end stage renal failure patients

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Discovering knowledge about key sequences for indexing time series cases in medical applications

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Abstract

Case-base maintenance typically involves the addition, removal or revision of cases, but can also include changes to the retrieval knowledge. In this paper, we consider the learning of the retrieval knowledge (organization) as well as the prototypes and the cases as case-based maintenance. We address this problem based on cases that have a structural case representation. Two approaches for organizing the case base are proposed. Both are based on approximate graph subsumption.