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Summary Case-based reasoning (CBR) is used when generalized knowledge is lacking. The method works on a set of cases formerly processed and stored in the case base. A new case is interpreted based on its similarity to cases in the case base. The closest case with its associated result is selected and presented as output of the system. Signal-interpreting systems for 1-d, 2-d, or 3-dimensional signals...
Summary In this chapter, we analyze and discuss the concept of similarity. Similarity plays an important role in many computer applications. These are often tasks that have either no precise input description or where the solution can only be approximated. We consider two main methodologies, case-based reasoning (CBR) and pattern recognition (PR). The specific tasks we deal with are mainly classification,...
Summary Assessing the similarity between cases is a prerequisite for many case-based reasoning tasks. This chapter centers on distance function learning for supervised similarity assessment. First a framework for supervised similarity assessment is introduced. Second, three supervised distance function learning approaches from the areas of pattern classification, supervised clustering, and information...
SummaryInducing similarity measures for the case based reasoning scheme through separable data transformations is considered in this chapter. Particular attention is paid to linear transformations of multidimensional data on visualising planes. Separable linear transformations are based both on solutions of eignevalue problems used in the principal componet analysis or in the discriminant analysis...
Graphs are a powerful and universal tool widely used in information processing. Numerous methods for graph analysis have been developed. Examples include the detection of Hamiltonian cycles, shortest paths, vertex coloring, graph drawing, and so on [5]. In particular, graph representations are extremely useful in image processing and understanding, which is the complex process of mapping the initially...
Summary Case-based reasoning (CBR) methodology stems from research on building computational memories capable of analogical reasoning, and require for that purpose specific composition and organization. This main task in CBR has triggered very significant research work and findings, which are summarized and analyzed in this article. In particular, since memory structures and organization rely on declarative...
Summary This chapter is concentrated with the performance characterization of a case-based reasoning (CBR) system. Based on the match score and nonmatch score computed from the cases in the case library, we develop a statistical model for prediction. We estimate the size of a subset of cases, called gallery size, that can generate the optimal error estimate and its confidence on a large population...
Summary This work presents a multiagent system for evaluating automatically the interaction that exists between the atmosphere and the ocean surface, monitoring and evaluating within the ocean carbon dioxide exchange process is a function requiring working with a great amount of data: satellite images and in situ Vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI)...
Summary In many industrial and medical diagnosis problems it is essential to investigate time series measurements collected to recognize existing or potential faults/diseases. Today this is usually done manually by humans. However the lengthy and complex nature of signals in practice often makes it a tedious and hard task to analyze and interpret available data properly even by experts with rich experiences...
Summary Already in the early stages of case-based reasoning (CBR) prototypes were considered as an interesting technique to structure the case base and to fill the knowledge gap between single cases and general knowledge. Unfortunately, later on prototypes never became a hot topic within the CBR community. However, for medical applications they have been used rather regularly, because they correspond...
Summary This chapter introduces a novel image-segmentation scheme based on case-based reasoning. Image segmentation is aimed at dividing an image into a number of different regions in such a way that each region is homogeneous with respect to a given property, but the union of any two adjacent regions is not. To reach this goal, a number of different approaches have been suggested in the literature,...
Summary Similarity-based image retrieval, which has become an important area of computer vision, is a part of the case-based reasoning scenario. In similarity-based retrieval, a query image is provided and similar images from a database are retrieved, usually in order of similarity. In this chapter, we discuss the use of similarity-based retrieval for biomedical data. In particular, we describe three...
Summary Image processing is an important focus area within case-based reasoning. CBR systems have been developed both to support image-centric functionality such as segmentation, as well as domain-specific imagery applications. In particular, case-based medical applications employ significant imagery elements to support tasks such as diagnosis and treatment planning. Whereas previous surveys have...
Summary High-retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. The user marks all the retrieved images as being either relevant or not, then the search engine exploits this relevance feedback to adapt the search to better meet user’s needs. The main difficulties in exploiting relevance information are (a) the gap between user perception...
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