In machine learning classification is the problem of identifying class/type of a given input quantity. Formally the problem can be stated like, we have a set of classes/types represented by:

C={t(1), t(2),…, t(m)}.

We have a set *P* of objects, each of which is described by a vector. All the objects of *P* have a unique class from *C*. From *P* we are given n objects (that is their representative vectors) *p(1)*, *p(2)*, …, *p(n)* (each *p(i)* is d-dimensional vector) and for each one of them *p(i)* the class is also given *c(i)*. These *n* vectors with their classes *( p(i) , c(i))* are called as the training data. We are given a distance measure *d( p1 , p2)* that gives the relevant distance between two vectors of *P*. Now, we are ...

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