Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 15
Sivu 103
... Transformation properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
... Transformation properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
Sivu 104
... transform each of the I1 - space vertices into one of the vertices of a P2 - dimensional hypercube . The transformation from I1 space to I2 space depends on the values of the weights in the second layer . For a given set of weights ...
... transform each of the I1 - space vertices into one of the vertices of a P2 - dimensional hypercube . The transformation from I1 space to I2 space depends on the values of the weights in the second layer . For a given set of weights ...
Sivu 131
... transformation where Z = QX Q = DiTt ( A.13 ) ( A.14 ) Using Eq . ( A - 14 ) we have p ( X ) = 1 ( 2π ) d / 2 ... transformation Q is a trans- formation which takes the ellipsoidal surfaces of constant probability density of X into ...
... transformation where Z = QX Q = DiTt ( A.13 ) ( A.14 ) Using Eq . ( A - 14 ) we have p ( X ) = 1 ( 2π ) d / 2 ... transformation Q is a trans- formation which takes the ellipsoidal surfaces of constant probability density of X into ...
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 |