Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 42
... Networks of Adaline " Neurons , " in Yovits , Jacobi , and Goldstein ( eds . ) , " Self - organizing Systems -1962 , " p . 442 , Spartan Books , Washington , D.C. , 1962 . 13 Brown , R .: Logical Properties of Adaptive Networks ...
... Networks of Adaline " Neurons , " in Yovits , Jacobi , and Goldstein ( eds . ) , " Self - organizing Systems -1962 , " p . 442 , Spartan Books , Washington , D.C. , 1962 . 13 Brown , R .: Logical Properties of Adaptive Networks ...
Sivu 95
... network . The properties of TLU networks are not yet fully understood . ( For example , expressions do not yet exist for the capacity of these networks nor training theorems for them . ) Nevertheless , it is believed that these networks ...
... network . The properties of TLU networks are not yet fully understood . ( For example , expressions do not yet exist for the capacity of these networks nor training theorems for them . ) Nevertheless , it is believed that these networks ...
Sivu 135
... networks of TLUS , 95 Learning matrix , 40 Learning the covariance matrix , 62 Learning the mean vector , 58 Learning without a teacher , 125 Lewis , 12 , 13 Likelihoods , 45 Linear classification , 20 Linear dichotomies , 20 , 67 ...
... networks of TLUS , 95 Learning matrix , 40 Learning the covariance matrix , 62 Learning the mean vector , 58 Learning without a teacher , 125 Lewis , 12 , 13 Likelihoods , 45 Linear classification , 20 Linear dichotomies , 20 , 67 ...
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 |