By Rosina O. Weber, Michael M. Richter
This ebook constitutes the refereed court cases of the seventh foreign convention on Case-Based Reasoning, ICCBR 2007, held in Belfast, Northern eire, united kingdom in August 2007.
The 15 revised complete study papers and 18 revised poster papers offered including three invited talks have been rigorously reviewed and chosen from sixty four submissions. The papers handle all present features of case-based reasoning and have unique theoretical examine, utilized examine, and deployed purposes with sensible, social, environmental or fiscal significance.
Read or Download Case-Based Reasoning Research and Development: 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast Northern Ireland, UK, August PDF
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These may impact the evolution of metadata associated with UserPreference, query formulation, data source usage and result ranking. The KSMM can be implemented as a relational database schema, which can be used to organize, store and interrelate the artifacts associated with a user query. The data can then be mined emergent properties related to the use of Knowledge Sifter resources. 4 Case-Based Knowledge Sifter Framework The original Knowledge Sifter [3] creates a repository of user queries and artifacts produced during the search process.
Yahoo Images supports the name and description for images; this allows the Ranking Agent to perform more precise evaluation for the semantic criteria. The Ranking Agent also uses the size of images contained in Yahoo Images metadata to filter images based on user preference, but the metadata does not contain the creation time of images which is a good measure of temporal aspect. 3 Emergent Semantics in Knowledge Sifter This section presents some notions related to emergent behavior and patterns that arise from 1) the functioning of Knowledge Sifter, and 2) the use of composable Web services to create reusable search frameworks.
The usual tool in such domains is an eager learning method inducing general domain knowledge. The main problem of these approaches is that sometimes the models have to be induced from a set of cases with high variability and the result is a set of rules that are too general to be useful for classification. An example of a lazy construction of a domain model is the Lazy Decision Trees (LDT) proposed by [17]. e. each time that a new problem has to be classified, the system reuses, if possible, the existing tree.