By Dr. Floyd J. Fowler, Dr. Thomas W. Mangione

A pragmatic consultant to generating standardized, and trustworthy, interviews, this quantity represents a mixing of social technology theories of interviewing dynamics, the authors' personal wide study on interview-related errors and learn proof from different famous methodologists.

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Why Case-Based Reasoning Is Attractive for Image Interpretation Petra Perner Institute of Computer Vision and Applied Computer Sciences Arno-Nitzsche-Str. de Abstract. The development of image interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility. The introduction of case-based reasoning (CBR) strategies can help to overcome these drawbacks. , images) and the problems mentioned above provide special requirements for CBR strategies.

Grimnes, M. (1996). A two layer case-based reasoning architecture for medical image understanding, In I. Smith & B. ) Advances in Case-Based Reasoning (pp. 164-178). Berlin: Springer Verlag. Perner, P. (1999). An architecture for a CBR image segmentation system. Journal of Engineering Application in Artificial Intelligence, 12(6), 749-759. Micarelli, A. , & Sansonetti, G. (2000). A case-based approach to image recognition, In E. Blanzieri & L. ) Advances in Case-Based Reasoning (pp. 443-454). Berlin: Springer Verlag.

The user can interact with SaxEx in the four main CBR subtasks. This new functionality requires that the use and combination of the two basic mechanisms—perspectives and preferences— in the Retrieve and Reuse subtasks must be parameterizable and dynamically modifiable. 3 Retrieval Perspectives Retrieval perspectives [2] are built by the identify subtask and can be used either by the search or the select subtask. Perspectives used by the search subtask will act as filters. Perspectives used by the select subtask will act only as a preference.

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