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 76
... networks of threshold devices connected through adjustable weights ( synapses ) as model nerve nets capable of learning . The main credit for the successful synthesis of these two concepts into the idea of a trainable TLU must go to ...
... networks of threshold devices connected through adjustable weights ( synapses ) as model nerve nets capable of learning . The main credit for the successful synthesis of these two concepts into the idea of a trainable TLU must go to ...
Sivu 113
... networks have been studied by Farley and Clark , Rosenblatt , Widrow , Brain et al . , and others . The simple a perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single ...
... networks have been studied by Farley and Clark , Rosenblatt , Widrow , Brain et al . , and others . The simple a perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |