Symbolic kernel discriminant analysis

Current technological progress in Hardware, Data Bases and Object Oriented languages implies the manipulation, stock and representation of objects with more and more complex data. The notion of Symbolic Objects is introduced on the base of Diday’s work and the necessity to be adapted to this notion appears for most recent classification methods. The aim of this paper is the adaptation of the classical Bayesian discrimination rule to the Symbolic Objects problematic. This will be performed by the prior probabilities’ estimation and by a kernel density estimation.