Using typicality theory to select the best match

Autor:	Alejandro Martins, Rosina Weber-Lee, Roberto Carlos dos Santos Pacheco, Ricardo Miranda Barcia
Rok vydání:	1996
Předmět:	Reasoning system Computer science business.industry Similarity heuristic Value (computer science) Machine learning computer.software_genre Task (project management) Set (abstract data type) Data set Selection (linguistics) Artificial intelligence Data mining business computer
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540619550 EWCBR
DOI:	10.1007/bfb0020629
Popis:	This paper focuses on the problem of choosing the best match among a set of retrieved cases. The Select step is the subtask of case retrieval that produces the case that suggests the solution for the input case. There are many different ways to accomplish this task and we propose an automatic means for it. Following the original motivation of paralleling the human similarity heuristic we argue that the selection of the best match is performed by humans choosing the solution that best represents the set of candidate solutions retrieved. The solution that best represents a given data set is the “most typical” solution. Therefore, we describe an application in a Case-Based Reasoning system using the Theory of Typicality to calculate the Most Typical Value of a given set to automatically perform the Select task. An example illustrates the application.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::434b15f35bc9cb058e0f3c8974e839cb https://doi.org/10.1007/bfb0020629 Zobrazit plný text záznamu