Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 13
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 × 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 × 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 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |