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LINEAR METHODS IN CLASSIFICATION AND LEARNING
NONLINEAR METHODS IN CLASSIFICATORY ANALYSIS
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adaptive sample set approximate arbitrary belong boundary chapter choice class membership classification classificatory decisions cluster coefficients constant coordinate values correlation cost covariance matrix decision procedure decision regions decision rule discussed distribution eigenvalues eigenvectors Euclidean distance example expressed F and G Figure frequencies Gaussian processes given in Eq given samples input interval intraset distances learning likelihood ratio linear combinations linear discriminants linear transformation machine machine learning maximization mean-square distance measurements member of class member of F minimization problem minimum noise number of dimensions number of samples numerical values observations optimum orthogonal output pattern recognition Perceptron polynomial probability densities probability of error properties quadratic form recognize regression line represented sample set construction sensory units set of points set of samples shown in Fig signal space similar solution speaker speaker recognition speech subclass techniques threshold tion Unsupervised learning unvoiced V-dimensional V-space variable vector space vn-k Vocoder voiced sounds waveforms