Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 40
Sivu 70
... W responds incorrectly to an augmented pattern vector Y. The weight vec- tor is then changed to a new weight vector W ' , as follows : W ' W ' = W + cY = W CY if Y belonged to category 1 if Y belonged to category 2 ( 4.5 ) where c is ...
... W responds incorrectly to an augmented pattern vector Y. The weight vec- tor is then changed to a new weight vector W ' , as follows : W ' W ' = W + cY = W CY if Y belonged to category 1 if Y belonged to category 2 ( 4.5 ) where c is ...
Sivu 83
... W , such that Y. W > 0 for all Y in y ' . For a fixed solution vector W let min Y. WA a Yey ' ( 5.11 ) where a > 0. Taking the dot product of the solution vector W with both sides of Eq . ( 5 · 10 ) yields Ŵk + 1 • W = • 1 Ŷ1 . W + Ŷ2 ...
... W , such that Y. W > 0 for all Y in y ' . For a fixed solution vector W let min Y. WA a Yey ' ( 5.11 ) where a > 0. Taking the dot product of the solution vector W with both sides of Eq . ( 5 · 10 ) yields Ŵk + 1 • W = • 1 Ŷ1 . W + Ŷ2 ...
Sivu 85
... W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at the origin . If a ...
... W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at the origin . If a ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 |