Nearest neighbors methods for support vector machines

An important part of Pattern Recognition deals with the problem of classification of data into a finite number of categories. In the usual setting of " supervised learning " , examples are given that consists of pairs, (X i , Y i), i ≤ n, where X i is the d-dimensional covariate vector and y i is the corresponding " category " in some finite set C. In the examples, y i is known! Based on these examples (" training data ") an algorithm must be produced that will be able to predict the category, given a new value of the covariate. 1 Credit Scoring: Deciding if a bank client will be a good payer of the loan he/she receives.