Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 97
... Committee machines Suppose we have training subsets Y1 and Y2 of augmented training pat- terms which are not linearly separable . That is , no vector W exists such ... committee " of weight vectors W1 LAYERED MACHINES 97 Committee machines,
... Committee machines Suppose we have training subsets Y1 and Y2 of augmented training pat- terms which are not linearly separable . That is , no vector W exists such ... committee " of weight vectors W1 LAYERED MACHINES 97 Committee machines,
Sivu 100
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUs in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUs in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
Sivu 113
... committee machine was first proposed by Ridgway.5 Note that the committee machine and the a perceptron have a similar structure ; they are both two - layer ma- chines . Each has one layer of trainable TLUs ; they differ only in which ...
... committee machine was first proposed by Ridgway.5 Note that the committee machine and the a perceptron have a similar structure ; they are both two - layer ma- chines . Each has one layer of trainable TLUs ; they differ only in which ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |