Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 11
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 ...
... 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 ...
Sivu 93
... Perceptron Theory , Cornell Aeronautical Laboratory Report VG - 1196 - G - 7 , Buffalo , New York , June , 1960 . 3 Block , H. D .: The Perceptron : A Model for Brain Functioning , I , Reviews of Modern Physics , vol . 34 , pp . 123–135 ...
... Perceptron Theory , Cornell Aeronautical Laboratory Report VG - 1196 - G - 7 , Buffalo , New York , June , 1960 . 3 Block , H. D .: The Perceptron : A Model for Brain Functioning , I , Reviews of Modern Physics , vol . 34 , pp . 123–135 ...
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 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 step subsidiary discriminant Suppose terns 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 |