Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 77
... perceptrons . 8 During this period Widrow was pursuing engineering applications of trainable TLUS which he called ... Perceptron : A Perceiving and Recognizing Automaton , Project PARA , Cornell Aeronautical Laboratory Report 85-460-1 ...
... perceptrons . 8 During this period Widrow was pursuing engineering applications of trainable TLUS which he called ... Perceptron : A Perceiving and Recognizing Automaton , Project PARA , Cornell Aeronautical Laboratory Report 85-460-1 ...
Sivu 78
... Perceptrons and the Theory of Brain Mechanisms , " Spartan Books , Washington , D.C. , 1961 . 7 Block , H .: The Perceptron : A Model for Brain Functioning , I. , Reviews of Modern Physics , vol . 34 , pp . 123–135 , January , 1962 . 8 ...
... Perceptrons and the Theory of Brain Mechanisms , " Spartan Books , Washington , D.C. , 1961 . 7 Block , H .: The Perceptron : A Model for Brain Functioning , I. , Reviews of Modern Physics , vol . 34 , pp . 123–135 , January , 1962 . 8 ...
Sivu 113
... perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single trainable TLU in the second layer . ( Rosenblatt speaks of the a perceptron as a three - layer structure because ...
... perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single trainable TLU in the second layer . ( Rosenblatt speaks of the a perceptron as a three - layer structure because ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 |