Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 19
Sivu 68
... plane divides one of the original R ( N − 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N − 1 , D − 1 ) new regions . This fact gives us the relation R ( N ...
... plane divides one of the original R ( N − 1 , D ) regions in the D - dimensional space into two parts . Therefore , the addition of the Nth plane can add at most R ( N − 1 , D − 1 ) new regions . This fact gives us the relation R ( N ...
Sivu 73
... plane 0,0,0 0,1,0 22 1,1,0 O Patterns requiring -1 response Patterns requiring +1 response FIGURE 4.3 A plane which correctly partitions eight three - dimensional patterns -2 2 Y 3 Y2 1 ሀ . = +1 F Response Threshold element Augmented ...
... plane 0,0,0 0,1,0 22 1,1,0 O Patterns requiring -1 response Patterns requiring +1 response FIGURE 4.3 A plane which correctly partitions eight three - dimensional patterns -2 2 Y 3 Y2 1 ሀ . = +1 F Response Threshold element Augmented ...
Sivu 107
... plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine is a ...
... plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine is a ...
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 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 |