Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... threshold element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1.5 as a basic model of a two ...
... threshold element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1.5 as a basic model of a two ...
Sivu 21
... Threshold element xd Pattern w d + 1 +1 Weights FIGURE 2.4 The threshold logic unit ( TLU ) The pattern dichotomizer with linear g ( X ) can be implemented ac- cording to the block diagram in Fig . 2.4 . Such a structure , consisting of ...
... Threshold element xd Pattern w d + 1 +1 Weights FIGURE 2.4 The threshold logic unit ( TLU ) The pattern dichotomizer with linear g ( X ) can be implemented ac- cording to the block diagram in Fig . 2.4 . Such a structure , consisting of ...
Sivu 41
... Threshold Units , Rome Air Development Center Technical Documentary Report RADC - TDR - 64-32 , February , 1964 . 8 : Geometrical and Statistical Properties of Linear Threshold Devices , Stanford Electronics Laboratories Technical ...
... Threshold Units , Rome Air Development Center Technical Documentary Report RADC - TDR - 64-32 , February , 1964 . 8 : Geometrical and Statistical Properties of Linear Threshold Devices , Stanford Electronics Laboratories Technical ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
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 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 |