Resumen de A survey of (pseudo-distance) functions for structured-data
Arturo Vicente Estruch Guitart, César Ferri Ramírez, José Hernández Orallo, M. José Ramírez Quintana
Learning from structured data is becoming increasingly important. Besides the well-known approaches which deals directly with complex data representation (inductive logic programming and multi relational data mining), recently new techniques have been proposed by upgrading propositional learning algorithms. Focusing on distance-based methods, they are extended by incorporating similarity functions defined over structured domains, for instance a k-NN algorithm solving a graph classification problem. Since a measure between objects is the essential component for this kind of methods, this paper consists of a brief survey about some of the recent similarity functions defined over common structured data (lists, sets, terms, etc.).
