Lab 2: Linear Discriminant Functions Objective To classify Iris datasets using linear discriminant functions. Background Linear discriminant functions are those functions which are either linear in the components of feature vector or linear in some given set of functions of r. Linear classifiers can be easily built using discriminant functions and does not need the knowledge on the underlying probability densities of the given data. Linear classifiers are attractive due to their simplicity and ideal for initial, trial classifiers. The classifier parameters can be computed using a set of training samples and minimizing a criterion function. A discriminant function g(x) that is linear in the component of the feature vector x can be written as: g(x) = w+x+ wo (1) where w is the weight vector and wo is the bias or threshold. In general for a two category class problem we decide on wi (class 1) by observing the feature vector x if g(x) > 0) and W2 (class 2) if g(x) 0) and W2 (class 2) if g(x)