Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 56
... matrix of each pattern class . We see that this case leads to linear discriminant functions and thus to linear machines . The first d weights employed by the ith discriminator are given by the values of the ... covariance matrices,
... matrix of each pattern class . We see that this case leads to linear discriminant functions and thus to linear machines . The first d weights employed by the ith discriminator are given by the values of the ... covariance matrices,
Sivu 58
... covariance matrix . The first step in its de- velopment is to form a matrix Q ; whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then a d X N , matrix ...
... covariance matrix . The first step in its de- velopment is to form a matrix Q ; whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then a d X N , matrix ...
Sivu 59
... covariance matrices are all known but for which the mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d ...
... covariance matrices are all known but for which the mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |