Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 6
Sivu 98
... majority of the weight vectors yield dot products having the correct sign . Therefore these three weight vectors can be employed by a committee of three TLUs in such a Y1 W W2 1 Y2 W3 Y 3 FIGURE 6.3 A commmittee of weight vectors way ...
... majority of the weight vectors yield dot products having the correct sign . Therefore these three weight vectors can be employed by a committee of three TLUs in such a Y1 W W2 1 Y2 W3 Y 3 FIGURE 6.3 A commmittee of weight vectors way ...
Sivu 100
... majority of the weight vectors have negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ( ) , W2 ( k ) , and Wp ( ) . 1 " " " In describing the rule for modifying the weight vectors we shall make use of ...
... majority of the weight vectors have negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ( ) , W2 ( k ) , and Wp ( ) . 1 " " " In describing the rule for modifying the weight vectors we shall make use of ...
Sivu 101
... majority of the committee TLUS to respond negatively , we adjust the 1⁄2 ( N✩ | + 1 ) weight vectors making the least- negative ( but not positive ) dot products with Y. If the weight vector W ( ) is among this set of 1⁄2 ( N1 | + 1 ) ...
... majority of the committee TLUS to respond negatively , we adjust the 1⁄2 ( N✩ | + 1 ) weight vectors making the least- negative ( but not positive ) dot products with Y. If the weight vector W ( ) is among this set of 1⁄2 ( N1 | + 1 ) ...
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 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 |