Hong-linh Truong, Maja Vukovic, et al.
ICDH 2024
Abstract: In building a knowledge‐based system, it is sometimes possible to save time by applying some machine learning process to a set of historical cases. In some problem domains, however, such cases may not be available. In addition, the classes, attributes and attribute values that comprise the partial domain model in terms of which cases are expressed may also not be available explicitly. In these circumstances, the repertory grid technique offers a single process for both building a partial domain model and generating a training set of examples. Alternatively, examples can be elicited directly. This paper explores the relationship between knowledge acquisition from examples and the repertory grid technique, and discusses the shared need for machine learning. Fragments of business‐strategy knowledge are used to illustrate the discussion. Copyright © 1992, Wiley Blackwell. All rights reserved
Hong-linh Truong, Maja Vukovic, et al.
ICDH 2024
Ran Iwamoto, Kyoko Ohara
ICLC 2023
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rakesh Mohan, Ramakant Nevatia
IEEE Transactions on Pattern Analysis and Machine Intelligence