Decision-making Processes in Pattern Recognition

adaptive applied approximate arbitrary assumed belong boundary chapter choice classification cluster coefficients common comparison computed consider constant constraint construction contained coordinate correlation corresponding cost Decide decision rule described determined developed dimensions discriminant discussed distance distribution equal error example expressed F and G Figure fitting frequencies function Gaussian given given in Eq human illustrated input instance interval introduced known learning likelihood ratio limitations linear machine matrix maximization mean mean-square distance measurements membership methods metric minimization noise nonlinear number of samples observations obtained operation optimum pattern recognition performed points polynomial practical probability densities problem procedure properties recognition recognize region regression represented requirement samples selected separate shown signal similar solution solved sounds speaker speech spoken square stored techniques theory threshold tion transformation unit values variable vector space voiced zeros