Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... values . These optimum estimates are meaningful , however , only when the unknown proba- bility values are themselves random variables with known probability distributions . As an example , consider the case of N successive Bernoulli ...
... values . These optimum estimates are meaningful , however , only when the unknown proba- bility values are themselves random variables with known probability distributions . As an example , consider the case of N successive Bernoulli ...
Sivu 50
... values of the TLU weights and threshold . 3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an ...
... values of the TLU weights and threshold . 3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an ...
Sivu 52
... variables x and x2 is more complicated * than that of Eq . ( 3.18 ) , but the general properties of the function are ... random variables . The assumption made in the previous footnote should now be generalized to σ122011022 . center of ...
... variables x and x2 is more complicated * than that of Eq . ( 3.18 ) , but the general properties of the function are ... random variables . The assumption made in the previous footnote should now be generalized to σ122011022 . center of ...
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 |