Learning machines: foundations of trainable pattern-classifying systems

1)-dimensional 9Ci and 9C2 adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix decision regions decision surfaces denote density function discussed dot products error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function random variables sample covariance matrix sequence Sy set 9C solution weight vectors Stanford subsets 9Ci Suppose training patterns training sequence training set training subsets values W*+i Wd+i weight point weight space weight vector Wi